Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

might signify the action of making coffee or tea, this could be differentiated by the time period of the activities.
Kang et al. [30] explained that Hierarchical HMM is more appropriate than any typical HMM when utilizing the feature of measuring the activity's period. The researchers considered that the time period of sub-activities in the lower hierarchical level should be lower than the main activity time period, as this main activity is modeled by the two more corresponding sub-activities. However, Chung et al. [31] modified the above by introducing the contextual characteristic of hierarchical configuration through Hierarchical Context-HMM, and applied three HMMs (λSC, λBR, λTR ) in diverse contexts to recognize activities. Duong et al. [32] designed a period model with distinct Coxian distribution and reported that their method is superior in computational cost as compared to any classical technique by unambiguously modeling the period. Their method needed a huge number of parameters for training and testing. Forkan et al. [33] identified two main issues with typical anomaly detection techniques. First, the inability of forecasting upcoming trends that stopped early detection of activities causing disease, and then another, the inclusion of solo context scenarios triggered false alarms for decision making. Subject sickness, e.g., diabetes, is not only because of their mature age, so there is a need to include another context apart from sugar level. Therefore, they integrated HMM with Fuzzy Logic to recognize activities with more than one contextual domain and generate forecasting trends by combining all the information. Wong et al. [34] developed that the finest method to extract profile data is to outline the most frequently visited place of a subject. Novaik et al. [35] applied a Self-Organizing Map to the group and formed activities from sub-activities. Once training is done, any activity that diverges from this group is considered as irregular. However, Wong et al. [34] detected that the system generates an incorrect alarm for abnormality recognition because resident's ADLs are asymmetrical, such as watching TV until midnight and sleeping long hours in the day time.
This paper proposes a unique anomaly detection method based on a heterogeneous sensing system for elderly people's ADL and ADL routine monitoring by sensor data fusion. The paper has the following main contributions below. First, proposing an effective sensor data fusion technique based on a wireless heterogeneous sensor network system. The push button indicator is used for recognition of specific daily activities, not as a primary input. The placement information bagged by the room-mounted sensors is utilized for the implication of a user's room-level ADL. Conferring to the commonly occurring ADLs, the system dexterously split the whole task of identifying all the distinct activities into definite room-based sub-activities. Since each sub-activity categorization model for each sub-event takes recognition of a few activities, the system can improve proficiency and precision. In wireless sensor data fusion, the locality information is necessary to activate the sub-activity models that are pre-trained by the other heterogeneous sensors. Second, in our exploration, we offer a novel attitude to attain significant efficiency from the smart home care framework. To give a reliable arrangement, we proposed and actualized an AAL based on an incorporated system to investigate the person's conduct on the basis of historical data, real-time freshly received data, and feedback received data. Third, to address the research challenges, we apply behavioral pattern generation through pipeline processing with pre-processing, activity recognition, and smoothing. We focus on isolation of the typical routine information from surprising information, which may cause a risk to Wellness.
We continue the research work by offering the AAL system methodology in Unit III, with the sub-section headings (a) Design and deployment of intelligent wireless sensor and networks (IWSN) (b) Preprocessed Sensor Data Acquisition (PSDA), (c) Activity Annotation, and (d) Wellness Indices Modeling. The investigational results and performance concerns in Unit IV have been represented. The extensions of present research for future work and conclusions with probable solutions towards matter resolution are drawn in Unit V.

Methodology: AAL Environment Approach and Setup
The framework was planned and actualized in two levels; hardware prototype development, followed by deployment and programming logic. At the prototype development and deployment, the diverse sensing units were developed and positioned to acquire multi-movement and multi-event sensory activation. The present intelligent remote sensing units were designed with Mesh topology, and the crude sensor activation was received by coordinator receiver module and gathered into a server that may be a local home gateway, as well as being cloud-based. In the present research, we have implemented a local home gateway approach for the server. The programming unit was sectioned into various stages, for example, information recording, information mining, and information stockpiling; these modules were responsible for correlating the change in lifestyle with the well-being of an individual in progressive or close time. The present system used a novel Wellness Protocol developed by us. The sensing nodes were intelligent enough to process and transmit the data according to the event and priority of sub-activity [18,19]. Figure 1 depicts the system approach from tiny sensor development to data transmission based on ISM band 2.4 GHz. The diagram shows the flow from data storage into the server to sensor data fusion and decision making information generation. All the node-processed sensory activation data was recorded to represent the events. The present research study offers an innovative sensory data preprocessing and segmentation methodology that combines the sensor physical characteristics based on events, location, and time correlation.
Sensors 2019, 19, x FOR PEER REVIEW 8 of 32 and the crude sensor activation was received by coordinator receiver module and gathered into a server that may be a local home gateway, as well as being cloud-based. In the present research, we have implemented a local home gateway approach for the server. The programming unit was sectioned into various stages, for example, information recording, information mining, and information stockpiling; these modules were responsible for correlating the change in lifestyle with the well-being of an individual in progressive or close time. The present system used a novel Wellness Protocol developed by us. The sensing nodes were intelligent enough to process and transmit the data according to the event and priority of sub-activity [18,19]. Figure 1 depicts the system approach from tiny sensor development to data transmission based on ISM band 2.4 GHz. The diagram shows the flow from data storage into the server to sensor data fusion and decision making information generation. All the node-processed sensory activation data was recorded to represent the events. The present research study offers an innovative sensory data preprocessing and segmentation methodology that combines the sensor physical characteristics based on events, location, and time correlation.

Smart Home Setup Based on IWSN
The present research system named a Wellness system was designed and executed in four different houses. Those houses were the residences of lone-living elderly persons. Figure 2 represents one of the houses where the Wellness-based smart home system was implemented. The Wellness smart home system offers a user-friendly and low maintenance AAL environment, without significant change in interior or exterior of the house. Moreover, it allows an elderly inhabitant to live in an uncontrolled homely condition.

Smart Home Setup Based on IWSN
The present research system named a Wellness system was designed and executed in four different houses. Those houses were the residences of lone-living elderly persons. Figure 2 represents one of the houses where the Wellness-based smart home system was implemented. The Wellness smart home system offers a user-friendly and low maintenance AAL environment, without significant change in interior or exterior of the house. Moreover, it allows an elderly inhabitant to live in an uncontrolled homely condition.
Sensors 2019, 19, x FOR PEER REVIEW 9 of 32 Figure 2. A more than seven-decade-old house where the Smart Aging system was installed without any significant changes in the house.
The sensing units are prone to physical damage; apart from that, the system is free from the complexity related to power supply and local home gateway data backup-storage. The algorithm for intelligent pre-processing of sensor activation at the node was developed and implemented on an Intel Galileo Generation-2 baseboard. On the basis of Wellness Protocol approach, sensory activations are transmitted to the local server via radio transceiver [18].
The crude sensor activations from sensors (not the sensing unit) are communicated to the baseboard Microcomputer for competent sampling and transmission controller algorithm. The present algorithm analyzes the sensor activations in advance, followed by being applied to Wellness packet encapsulation algorithm. The sampling-transmission control algorithm and packet encapsulation algorithm perform excess data reduction. These algorithms do it through categorization and detection of unavoidable sensor activation from indispensable sensor activation at the sensing unit level before it is communicated to the server. The outline of the AAL environment with sensor positioning is displayed in Figure 3. The sensors are positioned in such a style so that the system gets each indispensable sensor activation commencing in the household, which is valid to the well-being investigation of an elderly inhabitant. The sensing units are prone to physical damage; apart from that, the system is free from the complexity related to power supply and local home gateway data backup-storage. The algorithm for intelligent pre-processing of sensor activation at the node was developed and implemented on an Intel Galileo Generation-2 baseboard. On the basis of Wellness Protocol approach, sensory activations are transmitted to the local server via radio transceiver [18].
The crude sensor activations from sensors (not the sensing unit) are communicated to the baseboard Microcomputer for competent sampling and transmission controller algorithm. The present algorithm analyzes the sensor activations in advance, followed by being applied to Wellness packet encapsulation algorithm. The sampling-transmission control algorithm and packet encapsulation algorithm perform excess data reduction. These algorithms do it through categorization and detection of unavoidable sensor activation from indispensable sensor activation at the sensing unit level before it is communicated to the server. The outline of the AAL environment with sensor positioning is displayed in Figure 3. The sensors are positioned in such a style so that the system gets each indispensable sensor activation commencing in the household, which is valid to the well-being investigation of an elderly inhabitant. The movement sensors were installed near to doors, windows, corner of rooms, corner of kitchenette, and corner of restroom. Figure 4c represents the Passive Infrared Movement sensing unit deployed at the door and Figure 3. Figure 4d depicts the arrangement of the sensing unit in the kitchen for washing activities. An Electronics and Electrical (E & E) object sensor was plugged into all the electrical and electronic appliances to monitor the time duration of usage. Figure 4e-h represent the E & E unit connected to water cattle, rice cooker, microwave oven, and television, respectively. Figure 5b shows the deployment of the force sensor to measure the frequency of toilet usage and duration. To monitor housing stuff based on the close and open of closet, such as an almirah, office desk, and self-grooming table, wireless contact sensing units were designed. Figure  5b shows the flexiforce A301 used as a contact sensor at the shower door. The frequency of this object usage was monitored by connecting the contact sensing unit at the closet. It is a digital output on/off sensor. Figure 4a shows the outdoor temperature module based on a solar panel deployed at the window.
A push switch key module was a supplementary sensing unit for assessment; the data from it was not considered as primary because of errors during self-input by an elderly person. A manual push button sensor was designed to evaluate and cross-check the performance of activity recognition by the Wellness activity learning model. The movement sensors were installed near to doors, windows, corner of rooms, corner of kitchenette, and corner of restroom. Figure 4c represents the Passive Infrared Movement sensing unit deployed at the door and Figure 3. Figure 4d depicts the arrangement of the sensing unit in the kitchen for washing activities. An Electronics and Electrical (E & E) object sensor was plugged into all the electrical and electronic appliances to monitor the time duration of usage. Figure 4e-h represent the E & E unit connected to water cattle, rice cooker, microwave oven, and television, respectively. Figure 5b shows the deployment of the force sensor to measure the frequency of toilet usage and duration. To monitor housing stuff based on the close and open of closet, such as an almirah, office desk, and self-grooming table, wireless contact sensing units were designed. Figure 5b shows the flexiforce A301 used as a contact sensor at the shower door. The frequency of this object usage was monitored by connecting the contact sensing unit at the closet. It is a digital output on/off sensor. Figure 4a shows the outdoor temperature module based on a solar panel deployed at the window.
A push switch key module was a supplementary sensing unit for assessment; the data from it was not considered as primary because of errors during self-input by an elderly person. A manual push button sensor was designed to evaluate and cross-check the performance of activity recognition by the Wellness activity learning model.

Sensor Data Acquisition Module
Present research focuses on indoor ADL recognition for an elderly individual to identify their routine activities and separate the anomaly events. The present system does not directly monitor the toilet usage, hygiene, or bathing, though the system captures how often and how long the inhabitant uses the bathroom from the ambient sensors, contact sensors, and force sensors.
The data collection-related measures are accepted by Massey University Research Ethics Committee and Fudan University Ethics Committee. The sensor data acquisition is done in the elderly subject's homes based on a heterogeneous wireless sensors and networks. Considering older subjects' behavior in the function of an uncontrolled realistic environment, the existing elderly houses are converted into smart aging houses. The elderly subjects are encouraged and motivated to self-

Sensor Data Acquisition Module
Present research focuses on indoor ADL recognition for an elderly individual to identify their routine activities and separate the anomaly events. The present system does not directly monitor the toilet usage, hygiene, or bathing, though the system captures how often and how long the inhabitant uses the bathroom from the ambient sensors, contact sensors, and force sensors.
The data collection-related measures are accepted by Massey University Research Ethics Committee and Fudan University Ethics Committee. The sensor data acquisition is done in the elderly subject's homes based on a heterogeneous wireless sensors and networks. Considering older subjects' behavior in the function of an uncontrolled realistic environment, the existing elderly houses are converted into smart aging houses. The elderly subjects are encouraged and motivated to self-

Sensor Data Acquisition Module
Present research focuses on indoor ADL recognition for an elderly individual to identify their routine activities and separate the anomaly events. The present system does not directly monitor the toilet usage, hygiene, or bathing, though the system captures how often and how long the inhabitant uses the bathroom from the ambient sensors, contact sensors, and force sensors.
The data collection-related measures are accepted by Massey University Research Ethics Committee and Fudan University Ethics Committee. The sensor data acquisition is done in the elderly subject's homes based on a heterogeneous wireless sensors and networks. Considering older subjects' behavior in the function of an uncontrolled realistic environment, the existing elderly houses are converted into smart aging houses. The elderly subjects are encouraged and motivated to self-sufficiently execute each action in their individual way. They are free to go out and take a break from their daily routine. However, for the system, taking a break is also an activity. In the case of data interruption or error/loss, the valid data from the same activity are replaced.
The data collection process considers ninety days as one season of data. The present research work uses 43 weeks data. The present system does not use a defined fixed sampling rate, it uses dynamic sampling. The total average sample size for data is therefore about 16 GB for 2,675,655 sensor activations and 4 subjects. Though the system uses basic data preprocessing, data may comprise overlay, interferences, and noise between activities. The sensor data was raw, but sensory activation was processed before being transmitted from IWSN to Local Server. A Toshiba mobile workstation laptop was used as a Home gateway server. The mobile workstation was a 15.6-inch display, 1TB hard drive, 4 GB Ram, I5 processor. The user interface was developed for the data reception through com-port. Figure 5c shows the Local Server and Intel-board-based coordinator.

Data Pre-Processing by Advanced Belief Model
An era of IoT is where sensing information is linked to the internet. Wireless sensing units collect a huge amount of sensory activation data to apply to machine learning models, and decision or prediction of an event is done on a real-time basis. Nevertheless, the accuracy of ADL monitoring and forecasting is a function of the reliability of sensor data. Unluckily, received sensor data are erroneous and misleading. One of the possible reasons for degradation in sensing data reliability is missing data, repeated data, or erratic data. The missing data, repeated data, and erratic data lead to ambiguities, such as incompleteness, ignorance, and fuzziness. The resource limitations cause missing data. Malfunctioning of the sensing unit brings incompleteness to sensor data.
Thus, the error obstructs the suitable functioning of the smart home monitoring systems and degrades the reliability of data. Consequently, the recognition and detection related to wireless sensing data bring inevitable scrutiny, so it must be processed before being applied to a machine learning system to offer optimum accurate monitoring and forecasting alerts. Hence, it is indispensable to ensure the data reliability and accuracy before feeding it in any data mining and machine learning algorithm.
The majority of sensing units get interference caused by noise or errors; this could be calibrated during system design, though hardware malfunctioning, for example, creating consistent or stationary conduct, should be distinguished. The data received from sensing units are processed as the format of digital and analog series. The sensing unit generates two kinds of qualities, the first is an analog series and the second is a digital series. In the data types due to the atypical (flawed) conduct of sensors, it is possible that they cause underfitting (less information) or overfitting (overabundance information). Therefore, a model has been designed to separate this error, separating data from the inevitable useful data. That model is named as an Advanced Belief Model.

Advanced Belief Model for Analog Data Output
The analog data, such as temperature value received from a sensor, should be in the modeled characterized run. In the event that it goes past the threshold value or offers a consistent stationary value, at that point there is a high level of belief that shows gadget atypical conduct.
The Wellness threshold for analog-based sensing units is actualized through the linear regression approach. Let us assume, at time instance t, the Advanced Belief figure (BF t ) is termed through the difference (D t ) between the derived value (d t ) and the practical actual value (P t ) on standard deviation (σ) and sors 2019, 19, x FOR PEER REVIEW 12 of 32 fficiently execute each action in their individual way. They are free to go out and take a break from ir daily routine. However, for the system, taking a break is also an activity. In the case of data erruption or error/loss, the valid data from the same activity are replaced. The data collection process considers ninety days as one season of data. The present research rk uses 43 weeks data. The present system does not use a defined fixed sampling rate, it uses namic sampling. The total average sample size for data is therefore about 16 GB for 2,675,655 sensor ivations and 4 subjects. Though the system uses basic data preprocessing, data may comprise erlay, interferences, and noise between activities. The sensor data was raw, but sensory activation s processed before being transmitted from IWSN to Local Server. A Toshiba mobile workstation top was used as a Home gateway server. The mobile workstation was a 15.6-inch display, 1TB rd drive, 4 GB Ram, I5 processor. The user interface was developed for the data reception through -port. Figure 5c shows the Local Server and Intel-board-based coordinator.

. Data Pre-Processing by Advanced Belief Model
An era of IoT is where sensing information is linked to the internet. Wireless sensing units collect uge amount of sensory activation data to apply to machine learning models, and decision or diction of an event is done on a real-time basis. Nevertheless, the accuracy of ADL monitoring d forecasting is a function of the reliability of sensor data. Unluckily, received sensor data are oneous and misleading. One of the possible reasons for degradation in sensing data reliability is ssing data, repeated data, or erratic data. The missing data, repeated data, and erratic data lead to biguities, such as incompleteness, ignorance, and fuzziness. The resource limitations cause ssing data. Malfunctioning of the sensing unit brings incompleteness to sensor data.
Thus, the error obstructs the suitable functioning of the smart home monitoring systems and grades the reliability of data. Consequently, the recognition and detection related to wireless sing data bring inevitable scrutiny, so it must be processed before being applied to a machine rning system to offer optimum accurate monitoring and forecasting alerts. Hence, it is ispensable to ensure the data reliability and accuracy before feeding it in any data mining and chine learning algorithm.
The majority of sensing units get interference caused by noise or errors; this could be calibrated ring system design, though hardware malfunctioning, for example, creating consistent or tionary conduct, should be distinguished. The data received from sensing units are processed as format of digital and analog series. The sensing unit generates two kinds of qualities, the first is analog series and the second is a digital series. In the data types due to the atypical (flawed) duct of sensors, it is possible that they cause underfitting (less information) or overfitting erabundance information). Therefore, a model has been designed to separate this error, separating ta from the inevitable useful data. That model is named as an Advanced Belief Model.

Advanced Belief Model for Analog Data Output
The analog data, such as temperature value received from a sensor, should be in the modeled aracterized run. In the event that it goes past the threshold value or offers a consistent stationary lue, at that point there is a high level of belief that shows gadget atypical conduct.
The Wellness threshold for analog-based sensing units is actualized through the linear ression approach. Let us assume, at time instance t, the Advanced Belief figure (BFt) is termed ough the difference (Dt) between the derived value (dt) and the practical actual value (Pt) on ndard deviation (σ) and ɔ is confidence level (0.95). The derived value function is given by ɖ( ). dt = 1 ∑ ɖ( ) =0 (1) is confidence level (0.95). The derived value function is given by ficiently execute each action in their individual way. They are free to go out and take a break from ir daily routine. However, for the system, taking a break is also an activity. In the case of data rruption or error/loss, the valid data from the same activity are replaced. The data collection process considers ninety days as one season of data. The present research rk uses 43 weeks data. The present system does not use a defined fixed sampling rate, it uses amic sampling. The total average sample size for data is therefore about 16 GB for 2,675,655 sensor vations and 4 subjects. Though the system uses basic data preprocessing, data may comprise rlay, interferences, and noise between activities. The sensor data was raw, but sensory activation s processed before being transmitted from IWSN to Local Server. A Toshiba mobile workstation top was used as a Home gateway server. The mobile workstation was a 15.6-inch display, 1TB d drive, 4 GB Ram, I5 processor. The user interface was developed for the data reception through -port. Figure 5c shows the Local Server and Intel-board-based coordinator.

Data Pre-Processing by Advanced Belief Model
An era of IoT is where sensing information is linked to the internet. Wireless sensing units collect uge amount of sensory activation data to apply to machine learning models, and decision or diction of an event is done on a real-time basis. Nevertheless, the accuracy of ADL monitoring forecasting is a function of the reliability of sensor data. Unluckily, received sensor data are neous and misleading. One of the possible reasons for degradation in sensing data reliability is sing data, repeated data, or erratic data. The missing data, repeated data, and erratic data lead to biguities, such as incompleteness, ignorance, and fuzziness. The resource limitations cause sing data. Malfunctioning of the sensing unit brings incompleteness to sensor data. Thus, the error obstructs the suitable functioning of the smart home monitoring systems and rades the reliability of data. Consequently, the recognition and detection related to wireless sing data bring inevitable scrutiny, so it must be processed before being applied to a machine ning system to offer optimum accurate monitoring and forecasting alerts. Hence, it is ispensable to ensure the data reliability and accuracy before feeding it in any data mining and chine learning algorithm.
The majority of sensing units get interference caused by noise or errors; this could be calibrated ing system design, though hardware malfunctioning, for example, creating consistent or ionary conduct, should be distinguished. The data received from sensing units are processed as format of digital and analog series. The sensing unit generates two kinds of qualities, the first is analog series and the second is a digital series. In the data types due to the atypical (flawed) duct of sensors, it is possible that they cause underfitting (less information) or overfitting erabundance information). Therefore, a model has been designed to separate this error, separating a from the inevitable useful data. That model is named as an Advanced Belief Model.

Advanced Belief Model for Analog Data Output
The analog data, such as temperature value received from a sensor, should be in the modeled racterized run. In the event that it goes past the threshold value or offers a consistent stationary ue, at that point there is a high level of belief that shows gadget atypical conduct.
The Wellness threshold for analog-based sensing units is actualized through the linear ression approach. Let us assume, at time instance t, the Advanced Belief figure (BFt) is termed ugh the difference (Dt) between the derived value (dt) and the practical actual value (Pt) on dard deviation (σ) and ɔ is confidence level (0.95). The derived value function is given by ɖ( ). dt = 1 ∑ ɖ( ) =0 (1) (t).
action in their individual way. They are free to go out and take a break from wever, for the system, taking a break is also an activity. In the case of data ss, the valid data from the same activity are replaced.
process considers ninety days as one season of data. The present research ta. The present system does not use a defined fixed sampling rate, it uses total average sample size for data is therefore about 16 GB for 2,675,655 sensor cts. Though the system uses basic data preprocessing, data may comprise nd noise between activities. The sensor data was raw, but sensory activation eing transmitted from IWSN to Local Server. A Toshiba mobile workstation ome gateway server. The mobile workstation was a 15.6-inch display, 1TB 5 processor. The user interface was developed for the data reception through ws the Local Server and Intel-board-based coordinator.
by Advanced Belief Model ere sensing information is linked to the internet. Wireless sensing units collect ory activation data to apply to machine learning models, and decision or is done on a real-time basis. Nevertheless, the accuracy of ADL monitoring nction of the reliability of sensor data. Unluckily, received sensor data are ing. One of the possible reasons for degradation in sensing data reliability is data, or erratic data. The missing data, repeated data, and erratic data lead to ncompleteness, ignorance, and fuzziness. The resource limitations cause oning of the sensing unit brings incompleteness to sensor data. structs the suitable functioning of the smart home monitoring systems and of data. Consequently, the recognition and detection related to wireless itable scrutiny, so it must be processed before being applied to a machine ffer optimum accurate monitoring and forecasting alerts. Hence, it is the data reliability and accuracy before feeding it in any data mining and ithm. sing units get interference caused by noise or errors; this could be calibrated , though hardware malfunctioning, for example, creating consistent or uld be distinguished. The data received from sensing units are processed as d analog series. The sensing unit generates two kinds of qualities, the first is e second is a digital series. In the data types due to the atypical (flawed) is possible that they cause underfitting (less information) or overfitting ation). Therefore, a model has been designed to separate this error, separating useful data. That model is named as an Advanced Belief Model.

Model for Analog Data Output
uch as temperature value received from a sensor, should be in the modeled e event that it goes past the threshold value or offers a consistent stationary e is a high level of belief that shows gadget atypical conduct. eshold for analog-based sensing units is actualized through the linear et us assume, at time instance t, the Advanced Belief figure (BFt) is termed (Dt) between the derived value (dt) and the practical actual value (Pt) on and ɔ is confidence level (0.95). The derived value function is given by ɖ( ).
The analog data, such as temperature value received from a sensor, should be in the modeled characterized run. In the event that it goes past the threshold value or offers a consistent stationary value, at that point there is a high level of belief that shows gadget atypical conduct.
The Wellness threshold for analog-based sensing units is actualized through the linear regression approach. Let us assume, at time instance t, the Advanced Belief figure (BFt) is termed through the difference (Dt) between the derived value (dt) and the practical actual value (Pt) on standard deviation (σ) and ɔ is confidence level (0.95). The derived value function is given by ɖ( ). dt = 1 ∑ ɖ( ) =0 (1) |D t | when D t >

Advanced Belief Model for Analog Data Output
The analog data, such as temperature value received from a sensor, should b characterized run. In the event that it goes past the threshold value or offers a con value, at that point there is a high level of belief that shows gadget atypical conduct The Wellness threshold for analog-based sensing units is actualized thr regression approach. Let us assume, at time instance t, the Advanced Belief figur through the difference (Dt) between the derived value (dt) and the practical actu standard deviation (σ) and ɔ is confidence level (0.95). The derived value function i Otherwise BF t = 0.99 when D t ≤ data from the inevitable useful data. That model is named as an Advanced Belief M

Advanced Belief Model for Analog Data Output
The analog data, such as temperature value received from a sensor, should b characterized run. In the event that it goes past the threshold value or offers a con value, at that point there is a high level of belief that shows gadget atypical conduc The Wellness threshold for analog-based sensing units is actualized thr regression approach. Let us assume, at time instance t, the Advanced Belief figur through the difference (Dt) between the derived value (dt) and the practical act standard deviation (σ) and ɔ is confidence level (0.95

Advanced Belief Model for Digital Data Output
Digital or discrete valued sensory activations are derived through the Poisson distribution. The Advanced Belief value by Poisson distribution characterizes the probability of occurrence of an occasion in the defined time span. Assume that an independent event to happen "ʖ" times, over a predetermined time interim, at that point the probability of precisely "x" events is equivalent to The Advanced Belief model-based methodology is used for the detection of deviation from the derived value. The modeled value is contrasted with practical value and the distance between them is characterized as residue. The sensory activations are demonstrated by the probability distributions of parameters; data unit, the location of deployment, and time. For precise recognition of deviation, even the smallest residue must be resolved. Accordingly, the observation sampling rate must be sufficiently high for the detection of the lowest residue. By Nyquist Sampling hypothesis, the testing rate should be no less than double the (crest) rate of variation of sensing outputs.
The sensing units are deployed into the realistic home, the home conditions are uncontrolled, so different sources will include noise, for example, RF signals from household object usage. The noise is added to a data signal when the sensor event occurrence time is less than the defined sampling time.

Segmentation
For smoothing of later data mining and machine learning processes, the received analog and digital time series data are segmented into the suitable subwindows. To capture the routine circle of sub-activity, the customized window length according to subject and location of deployment is measured and recorded. The window length was between 14.5 s to 18.2 s for these four elderly subjects (the window specification was 128 and 256 samples) [36]. Additionally, to avoid the useful sensory data loss at the edges of the pair of contiguous sub-windows, the 50% overlap between adjacent sub-windows has been applied.
The total number of window segmentations ɲ for a time series data is given by where the Ł is the data length, Ş is the overlap size, and ƚ is the segmentation length. The window is split into ɲ sub-windows after the segmentation process.

Activity Modeling
The sensory facts from Wellness packet were extracted by local home gateway loaded with the Wellness sensor data acquisition algorithm. Wellness based activity learning model and activity mining algorithm are applied for pattern detection and anomaly detection.
The key motives of sensor data fusion and Wellness activity modeling are as below: • Dynamic sensor data fusion: The majority of research work of activity modeling is based on predefined sensor datasets. However, the proximate real-time ADL discovery founded on streaming sensory information is left unanswered [4,5,15,16]. Current research work represents an innovative, vibrant real-time sensory event segmentation methodology to offer the solution.

•
Dataset collection and variety: Most of the research work around the world does not use heterogeneous unobtrusive sensing in an uncontrolled environment. Moreover, they use one or two home datasets. The present AAL system was deployed in four elderly lone living houses.
In the present research, we had classified the activity detection module containing 3-stages to mine the evidence from sensory activations; the stages were as below: 1. Event Occurrence Stage (1): The present stage comprised all types of sensory stimulation caused " times, over a predetermined time interim, at that point the probability of precisely "x" events is equivalent to BF x, Assume that an independent event to happen "ʖ" times, over a interim, at that point the probability of precisely "x" events is equivalent to Belief model-based methodology is used for the detection of deviation from the modeled value is contrasted with practical value and the distance between them esidue. The sensory activations are demonstrated by the probability distributions unit, the location of deployment, and time. For precise recognition of deviation, esidue must be resolved. Accordingly, the observation sampling rate must be the detection of the lowest residue. By Nyquist Sampling hypothesis, the testing ss than double the (crest) rate of variation of sensing outputs. its are deployed into the realistic home, the home conditions are uncontrolled, so ll include noise, for example, RF signals from household object usage. The noise ignal when the sensor event occurrence time is less than the defined sampling of later data mining and machine learning processes, the received analog and ata are segmented into the suitable subwindows. To capture the routine circle of stomized window length according to subject and location of deployment is rded. The window length was between 14.5 s to 18.2 s for these four elderly w specification was 128 and 256 samples) [36]. Additionally, to avoid the useful t the edges of the pair of contiguous sub-windows, the 50% overlap between ws has been applied. er of window segmentations ɲ for a time series data is given by ɲ = Ł−Ş ƚ−Ş (5) ata length, Ş is the overlap size, and ƚ is the segmentation length. The window is ows after the segmentation process. g ts from Wellness packet were extracted by local home gateway loaded with the ta acquisition algorithm. Wellness based activity learning model and activity e applied for pattern detection and anomaly detection. s of sensor data fusion and Wellness activity modeling are as below: r data fusion: The majority of research work of activity modeling is based on prer datasets. However, the proximate real-time ADL discovery founded on ory information is left unanswered [4,5,15,16]. Current research work represents vibrant real-time sensory event segmentation methodology to offer the solution. ion and variety: Most of the research work around the world does not use unobtrusive sensing in an uncontrolled environment. Moreover, they use one or sets. The present AAL system was deployed in four elderly lone living houses. Assume that an independent event to happen "ʖ" times, over a ined time interim, at that point the probability of precisely "x" events is equivalent to Advanced Belief model-based methodology is used for the detection of deviation from the alue. The modeled value is contrasted with practical value and the distance between them erized as residue. The sensory activations are demonstrated by the probability distributions eters; data unit, the location of deployment, and time. For precise recognition of deviation, smallest residue must be resolved. Accordingly, the observation sampling rate must be ly high for the detection of the lowest residue. By Nyquist Sampling hypothesis, the testing ld be no less than double the (crest) rate of variation of sensing outputs. sensing units are deployed into the realistic home, the home conditions are uncontrolled, so sources will include noise, for example, RF signals from household object usage. The noise to a data signal when the sensor event occurrence time is less than the defined sampling entation smoothing of later data mining and machine learning processes, the received analog and e series data are segmented into the suitable subwindows. To capture the routine circle of ity, the customized window length according to subject and location of deployment is and recorded. The window length was between 14.5 s to 18.2 s for these four elderly the window specification was 128 and 256 samples) [36]. Additionally, to avoid the useful ata loss at the edges of the pair of contiguous sub-windows, the 50% overlap between ub-windows has been applied. total number of window segmentations ɲ for a time series data is given by Ł is the data length, Ş is the overlap size, and ƚ is the segmentation length. The window is ɲ sub-windows after the segmentation process.
ity Modeling sensory facts from Wellness packet were extracted by local home gateway loaded with the sensor data acquisition algorithm. Wellness based activity learning model and activity gorithm are applied for pattern detection and anomaly detection. key motives of sensor data fusion and Wellness activity modeling are as below: amic sensor data fusion: The majority of research work of activity modeling is based on preed sensor datasets. However, the proximate real-time ADL discovery founded on ming sensory information is left unanswered [4,5,15,16]. Current research work represents novative, vibrant real-time sensory event segmentation methodology to offer the solution. set collection and variety: Most of the research work around the world does not use rogeneous unobtrusive sensing in an uncontrolled environment. Moreover, they use one or home datasets. The present AAL system was deployed in four elderly lone living houses. x!
The Advanced Belief model-based methodology is used for the detection of deviation from the derived value. The modeled value is contrasted with practical value and the distance between them is characterized as residue. The sensory activations are demonstrated by the probability distributions of parameters; data unit, the location of deployment, and time. For precise recognition of deviation, even the smallest residue must be resolved. Accordingly, the observation sampling rate must be sufficiently high for the detection of the lowest residue. By Nyquist Sampling hypothesis, the testing rate should be no less than double the (crest) rate of variation of sensing outputs.
The sensing units are deployed into the realistic home, the home conditions are uncontrolled, so different sources will include noise, for example, RF signals from household object usage. The noise is added to a data signal when the sensor event occurrence time is less than the defined sampling time.

Segmentation
For smoothing of later data mining and machine learning processes, the received analog and digital time series data are segmented into the suitable subwindows. To capture the routine circle of sub-activity, the customized window length according to subject and location of deployment is measured and recorded. The window length was between 14.5 s to 18.2 s for these four elderly subjects (the window specification was 128 and 256 samples) [36]. Additionally, to avoid the useful sensory data loss at the edges of the pair of contiguous sub-windows, the 50% overlap between adjacent sub-windows has been applied.
The total number of window segmentations

Advanced Belief Model for Digital Data Output
Digital or discrete valued sensory activations are derived through the Poisson distribution. The Advanced Belief value by Poisson distribution characterizes the probability of occurrence of an occasion in the defined time span. Assume that an independent event to happen "ʖ" times, over a predetermined time interim, at that point the probability of precisely "x" events is equivalent to The Advanced Belief model-based methodology is used for the detection of deviation from the derived value. The modeled value is contrasted with practical value and the distance between them is characterized as residue. The sensory activations are demonstrated by the probability distributions of parameters; data unit, the location of deployment, and time. For precise recognition of deviation, even the smallest residue must be resolved. Accordingly, the observation sampling rate must be sufficiently high for the detection of the lowest residue. By Nyquist Sampling hypothesis, the testing rate should be no less than double the (crest) rate of variation of sensing outputs.
The sensing units are deployed into the realistic home, the home conditions are uncontrolled, so different sources will include noise, for example, RF signals from household object usage. The noise is added to a data signal when the sensor event occurrence time is less than the defined sampling time.

Segmentation
For smoothing of later data mining and machine learning processes, the received analog and digital time series data are segmented into the suitable subwindows. To capture the routine circle of sub-activity, the customized window length according to subject and location of deployment is measured and recorded. The window length was between 14.5 s to 18.2 s for these four elderly subjects (the window specification was 128 and 256 samples) [36]. Additionally, to avoid the useful sensory data loss at the edges of the pair of contiguous sub-windows, the 50% overlap between adjacent sub-windows has been applied.
The total number of window segmentations ɲ for a time series data is given by where the Ł is the data length, Ş is the overlap size, and ƚ is the segmentation length. The window is split into ɲ sub-windows after the segmentation process.

Activity Modeling
The sensory facts from Wellness packet were extracted by local home gateway loaded with the Wellness sensor data acquisition algorithm. Wellness based activity learning model and activity mining algorithm are applied for pattern detection and anomaly detection.
The key motives of sensor data fusion and Wellness activity modeling are as below: • Dynamic sensor data fusion: The majority of research work of activity modeling is based on predefined sensor datasets. However, the proximate real-time ADL discovery founded on streaming sensory information is left unanswered [4,5,15,16]. Current research work represents an innovative, vibrant real-time sensory event segmentation methodology to offer the solution.

•
Dataset collection and variety: Most of the research work around the world does not use heterogeneous unobtrusive sensing in an uncontrolled environment. Moreover, they use one or two home datasets. The present AAL system was deployed in four elderly lone living houses.
In the present research, we had classified the activity detection module containing 3-stages to mine the evidence from sensory activations; the stages were as below:

Advanced Belief Model for Digital Data Output
Digital or discrete valued sensory activations are derived through the Poisson distribution. The Advanced Belief value by Poisson distribution characterizes the probability of occurrence of an occasion in the defined time span. Assume that an independent event to happen "ʖ" times, over a predetermined time interim, at that point the probability of precisely "x" events is equivalent to The Advanced Belief model-based methodology is used for the detection of deviation from the derived value. The modeled value is contrasted with practical value and the distance between them is characterized as residue. The sensory activations are demonstrated by the probability distributions of parameters; data unit, the location of deployment, and time. For precise recognition of deviation, even the smallest residue must be resolved. Accordingly, the observation sampling rate must be sufficiently high for the detection of the lowest residue. By Nyquist Sampling hypothesis, the testing rate should be no less than double the (crest) rate of variation of sensing outputs.
The sensing units are deployed into the realistic home, the home conditions are uncontrolled, so different sources will include noise, for example, RF signals from household object usage. The noise is added to a data signal when the sensor event occurrence time is less than the defined sampling time.

Segmentation
For smoothing of later data mining and machine learning processes, the received analog and digital time series data are segmented into the suitable subwindows. To capture the routine circle of sub-activity, the customized window length according to subject and location of deployment is measured and recorded. The window length was between 14.5 s to 18.2 s for these four elderly subjects (the window specification was 128 and 256 samples) [36]. Additionally, to avoid the useful sensory data loss at the edges of the pair of contiguous sub-windows, the 50% overlap between adjacent sub-windows has been applied.
The total number of window segmentations ɲ for a time series data is given by where the Ł is the data length, Ş is the overlap size, and ƚ is the segmentation length. The window is split into ɲ sub-windows after the segmentation process.

Activity Modeling
The sensory facts from Wellness packet were extracted by local home gateway loaded with the Wellness sensor data acquisition algorithm. Wellness based activity learning model and activity mining algorithm are applied for pattern detection and anomaly detection.
The key motives of sensor data fusion and Wellness activity modeling are as below: • Dynamic sensor data fusion: The majority of research work of activity modeling is based on predefined sensor datasets. However, the proximate real-time ADL discovery founded on streaming sensory information is left unanswered [4,5,15,16]. Current research work represents an innovative, vibrant real-time sensory event segmentation methodology to offer the solution.

•
Dataset collection and variety: Most of the research work around the world does not use heterogeneous unobtrusive sensing in an uncontrolled environment. Moreover, they use one or two home datasets. The present AAL system was deployed in four elderly lone living houses.
In the present research, we had classified the activity detection module containing 3-stages to where the Ł is the data length,Ş is the overlap size, and ł is the segmentation length. The window is split into Assume that an independent event to happen "ʖ" times, over a terim, at that point the probability of precisely "x" events is equivalent to lief model-based methodology is used for the detection of deviation from the deled value is contrasted with practical value and the distance between them due. The sensory activations are demonstrated by the probability distributions it, the location of deployment, and time. For precise recognition of deviation, due must be resolved. Accordingly, the observation sampling rate must be e detection of the lowest residue. By Nyquist Sampling hypothesis, the testing han double the (crest) rate of variation of sensing outputs. are deployed into the realistic home, the home conditions are uncontrolled, so nclude noise, for example, RF signals from household object usage. The noise al when the sensor event occurrence time is less than the defined sampling later data mining and machine learning processes, the received analog and are segmented into the suitable subwindows. To capture the routine circle of mized window length according to subject and location of deployment is d. The window length was between 14.5 s to 18.2 s for these four elderly pecification was 128 and 256 samples) [36]. Additionally, to avoid the useful e edges of the pair of contiguous sub-windows, the 50% overlap between has been applied. of window segmentations ɲ for a time series data is given by length, Ş is the overlap size, and ƚ is the segmentation length. The window is s after the segmentation process.
from Wellness packet were extracted by local home gateway loaded with the acquisition algorithm. Wellness based activity learning model and activity pplied for pattern detection and anomaly detection. f sensor data fusion and Wellness activity modeling are as below: ata fusion: The majority of research work of activity modeling is based on preatasets. However, the proximate real-time ADL discovery founded on information is left unanswered [4,5,15,16]. Current research work represents rant real-time sensory event segmentation methodology to offer the solution. and variety: Most of the research work around the world does not use sub-windows after the segmentation process.

Activity Modeling
The sensory facts from Wellness packet were extracted by local home gateway loaded with the Wellness sensor data acquisition algorithm. Wellness based activity learning model and activity mining algorithm are applied for pattern detection and anomaly detection.
The key motives of sensor data fusion and Wellness activity modeling are as below: • Dynamic sensor data fusion: The majority of research work of activity modeling is based on pre-defined sensor datasets. However, the proximate real-time ADL discovery founded on streaming sensory information is left unanswered [4,5,15,16]. Current research work represents an innovative, vibrant real-time sensory event segmentation methodology to offer the solution.
• Dataset collection and variety: Most of the research work around the world does not use heterogeneous unobtrusive sensing in an uncontrolled environment. Moreover, they use one or two home datasets. The present AAL system was deployed in four elderly lone living houses.
In the present research, we had classified the activity detection module containing 3-stages to mine the evidence from sensory activations; the stages were as below: 1.
Event Occurrence Stage (1): The present stage comprised all types of sensory stimulation caused by activity accomplished by the elderly inhabitant. The sensory stimulation data was unitless. Thus, it would not generate the behavioral measurements in the current form. The sensor activation records were referred to the higher stage of circumstantial discovery.

2.
Circumstantial Factor Mining Stage (2): The circumstantial stage distinguished the elementary activities on the foundation of locality, period, and situation. The elementary activities were directed toward superior stage detection. This stage belongs to the sub-activity data. 3.
ADL Recognition Stage (3): The elementary ADLs were categorized and interrelated, dependent on circumstantial figures for pattern discovery.
The present sensor data fusion approach is the heart of the smart aging system, which uses the very first sensory activation information (either 0 or 1) to trigger the subclassification module. The sub-modules are pre-trained by the corresponding heterogeneous sensing data assigned to a specific localization.
For instance, when room "R1" is distinguished as triggered and occupied, then over the next time instance, only the sub module "R1" is initiated and functions as the learning activity. Subsequently, each sub-module is accountable for perceiving fewer ADL compared with the situations of perceiving all the characterized ADL, without applying the entire model of sensor data fusion. By doing this, the activity recognition precision can be enhanced without extra learning. The framework changes to "the entire model" mode to manage the circumstance when there is more than one inhabitant is distinguished, and are caught in the meantime. "The entire model" perceives all the characterized activities together by utilizing the heterogeneous sensing information. Figure 6 shows a subject's day by day routine construed from the room-level sensory data of object usage and movements, which can disclose to us when, to what extent, and how regularly the subject remains in particular rooms. Figure 6 similarly gives the points of interest that the individual under monitoring gets up in the room at around the same time in the morning and starts the typical routine, went to bed at the night, and interacts with various household stuff, and so on. Moreover, the room-level everyday routine over quite a while can uncover whether the subject could effectively sort out a day by day life, or whether the subject is driving an irregular routine contrasted with the usual ADL schedule.
To data mine the information from the heterogeneous sensors, we propose a straightforward, however viable, information combination technique, shown in Figures 6 and 7 and Table 1. The strategy depends on the accompanying presumption: a few activities can be restricted in a particular localization in the view of places of occurrence, e.g., cooking is exceptionally incomprehensible as occurring in a washroom, and teeth brushing may not occur in a room. Here, the subject's localization information can be utilized to trigger the room-based sub-models. Subsequently, after fusing the first sensor activation of a particular activity to the other most relevant sensory information, the entire classification classifier transforms into parallel-functioning sub-classifiers.
The ADL is extraordinarily unrelated and composite. Thus, merging the diversity of practices, lifestyle, and proficiencies in the course of generating ADLs is problematic. Therefore, the outline of executing the regular routine can be different from one elderly inhabitant to another. In the data-driven attitude of assisted technology-based smart aging feature mining is the unescapable stepladders for behavioral and activity discovery, which mines the evidence from server-stored sensory activations. Such statistics cover the position, period, spell of sensory stimulation, ambient circumstance, the interaction of an individual with a household object, and movement. The activity modeling diagram is shown in Figure 6. An activity is a sequence of sub-activities, for an example, preparing the toast includes taking toast from the refrigerator and placing toast in the toaster, followed by plugging it into the power supply, and washing the dishes in the wash basin. A quadratic sketch of modeling events is shown in Figure 7. The activity labeling and marking for different activities for 24 h is shown in Table 1.        Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation.
To offer the best possible solution, we designed Wellness grades, Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1. Sensor data fusion has been performed to recognize the sub-activity and ADL activity modeling. Table 2 shows the sensor activation collected in the server laptop for f elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given in in a user-friendly format, such as a number or well-being index. It is hard for a care understand anything precisely through the position of deployment or node ID of senso To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formu was based on non-usage as well as the inactive interval of household stuff or events, wh was designed through the usage as well as the active interval of household stuff or ev and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1 inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded inclusion of multiple observations of seasonal deviations and random days. Th remarkably reduced the false positive alert massages [6]. In the upgraded Wellness seasonal deviation observations had been familiarized by the time series modeling. The b method in equations was introduced by dynamic sensor data fusion and time series pri where Ϣ1 well-being function was formulated over the inactive spell duration of hous the elderly inhabitant; t was the definite spell duration of idleness (inactive) of a household; TIN was ultimate-idleness interval and outside this may grounds abnorma that duration was calculated from past observations. For the regular life of an individu be 1.  Sensor data fusion has been performed to recognize the sub-activity and ADL activity modeling. Table 2 shows the sensor activation collected in the server laptop for f elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given in in a user-friendly format, such as a number or well-being index. It is hard for a care understand anything precisely through the position of deployment or node ID of sensor To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formu was based on non-usage as well as the inactive interval of household stuff or events, wh was designed through the usage as well as the active interval of household stuff or ev and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1 inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded inclusion of multiple observations of seasonal deviations and random days. Th remarkably reduced the false positive alert massages [6]. In the upgraded Wellness seasonal deviation observations had been familiarized by the time series modeling. The b method in equations was introduced by dynamic sensor data fusion and time series pri where Ϣ1 well-being function was formulated over the inactive spell duration of hous the elderly inhabitant; t was the definite spell duration of idleness (inactive) of al household; TIN was ultimate-idleness interval and outside this may grounds abnorma that duration was calculated from past observations. For the regular life of an individu be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of househo elderly inhabitant; TA was the definite spell duration of activity of all or specific house an ultimate-active interval and outside this may be the grounds for an abnormality si duration was calculated from past observations. For the regular life of an individual, Ϣ2 The abnormality is only calculated when TA > TN. The regular duration of househo 2 was designed through the usage as well as the active interval of household stuff or events. The Sensor data fusion has been performed to recognize the sub-activity and ADL activity modeling. Table 2 shows the sensor activation collected in the server laptop for f elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given in in a user-friendly format, such as a number or well-being index. It is hard for a care understand anything precisely through the position of deployment or node ID of senso To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formu was based on non-usage as well as the inactive interval of household stuff or events, wh was designed through the usage as well as the active interval of household stuff or ev and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1 inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded inclusion of multiple observations of seasonal deviations and random days. Th remarkably reduced the false positive alert massages [6]. In the upgraded Wellness seasonal deviation observations had been familiarized by the time series modeling. The b method in equations was introduced by dynamic sensor data fusion and time series pri where Ϣ1 well-being function was formulated over the inactive spell duration of hous the elderly inhabitant; t was the definite spell duration of idleness (inactive) of a household; TIN was ultimate-idleness interval and outside this may grounds abnorma that duration was calculated from past observations. For the regular life of an individu be 1.    Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an 1 and Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an 2 were equivalent to 1, the elderly inhabitant was well.
The functions delling and Detection Methodology ls and caregivers diagnose and support only if the given information is uch as a number or well-being index. It is hard for a care provider to ly through the position of deployment or node ID of sensory activation. ution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 ell as the inactive interval of household stuff or events, whereas the Ϣ2 sage as well as the active interval of household stuff or events. The Ϣ1 unctions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly   dices Modelling and Detection Methodology fessionals and caregivers diagnose and support only if the given information is rmat, such as a number or well-being index. It is hard for a care provider to g precisely through the position of deployment or node ID of sensory activation. sible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 sage as well as the inactive interval of household stuff or events, whereas the Ϣ2 gh the usage as well as the active interval of household stuff or events. The Ϣ1 ilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly    Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness ctivity modeling. Table 2 shows the sensor activation collected in the server laptop for four different lderly houses, and the number of ADL recognized successfully from it. .

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is n a user-friendly format, such as a number or well-being index. It is hard for a care provider to nderstand anything precisely through the position of deployment or node ID of sensory activation. o offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 as based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 as designed through the usage as well as the active interval of household stuff or events. The Ϣ1 nd Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly nhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the nclusion of multiple observations of seasonal deviations and random days. The upgrading emarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the easonal deviation observations had been familiarized by the time series modeling. The below offered ethod in equations was introduced by dynamic sensor data fusion and time series principle.
here Ϣ1 well-being function was formulated over the inactive spell duration of household use by he elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific ousehold; TIN was ultimate-idleness interval and outside this may grounds abnormality situation hat duration was calculated from past observations. For the regular life of an individual Ϣ1 should e 1.  etection Methodology vers diagnose and support only if the given information is ber or well-being index. It is hard for a care provider to he position of deployment or node ID of sensory activation. signed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 active interval of household stuff or events, whereas the Ϣ2 as the active interval of household stuff or events. The Ϣ1 refore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly BASIC indices, those were later upgraded through the seasonal deviations and random days. The upgrading lert massages [6]. In the upgraded Wellness function, the familiarized by the time series modeling. The below offered ynamic sensor data fusion and time series principle.  Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the 1 should be 1. Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness ivity modeling. Table 2 shows the sensor activation collected in the server laptop for four different erly houses, and the number of ADL recognized successfully from it.

. Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is a user-friendly format, such as a number or well-being index. It is hard for a care provider to derstand anything precisely through the position of deployment or node ID of sensory activation. offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 s based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 s designed through the usage as well as the active interval of household stuff or events. The Ϣ1 d Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly abitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the lusion of multiple observations of seasonal deviations and random days. The upgrading arkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the sonal deviation observations had been familiarized by the time series modeling. The below offered thod in equations was introduced by dynamic sensor data fusion and time series principle.
ere Ϣ1 well-being function was formulated over the inactive spell duration of household use by elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific usehold; TIN was ultimate-idleness interval and outside this may grounds abnormality situation t duration was calculated from past observations. For the regular life of an individual Ϣ1 should 1.
where 16  etection Methodology vers diagnose and support only if the given information is ber or well-being index. It is hard for a care provider to he position of deployment or node ID of sensory activation. signed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 active interval of household stuff or events, whereas the Ϣ2 as the active interval of household stuff or events. The Ϣ1 erefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly BASIC indices, those were later upgraded through the seasonal deviations and random days. The upgrading lert massages [6]. In the upgraded Wellness function, the familiarized by the time series modeling. The below offered dynamic sensor data fusion and time series principle.  Sensor data fusion has been performed to recognize the sub-activity and ADL by Wel activity modeling. Table 2 shows the sensor activation collected in the server laptop for four diffe elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given informati in a user-friendly format, such as a number or well-being index. It is hard for a care provid understand anything precisely through the position of deployment or node ID of sensory activa To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation o was based on non-usage as well as the inactive interval of household stuff or events, whereas th was designed through the usage as well as the active interval of household stuff or events. Th and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the eld inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through inclusion of multiple observations of seasonal deviations and random days. The upgra remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function seasonal deviation observations had been familiarized by the time series modeling. The below off method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household us the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or spe household; TIN was ultimate-idleness interval and outside this may grounds abnormality situa 2 should be 1. The abnormality is only calculated when T A > T N . The regular duration of household use of an individual specifies how effectively one performs everyday indoor work. Conferring to cyclical variations, the elderly individual's behavior and the spell duration of object use vary but are not considered an abnormality situation. It should not be designated as a cautionary alert. For instance, the elderly individual's daily activities would not be similar from rainy to winter season; there will be a substantial transformation in the activities. Therefore, a behavioral adjustment must be recognized and detached from the abnormality symptom.  Methodology agnose and support only if the given information is well-being index. It is hard for a care provider to tion of deployment or node ID of sensory activation. Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 nterval of household stuff or events, whereas the Ϣ2 active interval of household stuff or events. The Ϣ1 , when Ϣ1 and Ϣ2 were equivalent to 1, the elderly C indices, those were later upgraded through the al deviations and random days. The upgrading ssages [6]. In the upgraded Wellness function, the rized by the time series modeling. The below offered ic sensor data fusion and time series principle.
The ultimate inactive household practice was assessed by the recent times recorded sensory episode factors {β (S t − S t−1 ) + (1 − β)(T I N t−1 )}, and the episode arose throughout the matching term in preceding the year β S t − S t−1 + (1 − β ) T I N t−1 .
The cyclical trend for ultimate inactive household practice was assessed by the recent times recorded sensory episode factors {γ (x t − S t ) + (1 − γ)(C t−L )}, and the episode arose throughout the matching term in the preceding year γ (x t − S t ) + (1 − γ ) C t−L .
The smoothed observation for ultimate active household use was assessed by the recent times recorded sensory episodes {α (x t − C Nt−L ) + (1 − α)(S Nt−1 − T N t−1 )}, and the episodes happened throughout the matching term in preceding the year α x t − C Nt−L + (1 − α ) S Nt−1 + T N t−1 .
The ultimate active household practice was assessed by the recent times recorded sensory episode factors {β (S Nt − S Nt−1 ) + (1 − β)(T N t−1 )}, and the episodes happened throughout the matching term in preceding the year β S Nt − S Nt−1 + (1 − β ) T N t−1 . Sensors The cyclical trend for ultimate active household practice was assessed by the recent times recorded sensory episode factors {γ (x t − S Nt ) + (1 − γ)(C Nt−L )}, and the episodes happened throughout the matching term in preceding the year γ x t − S Nt + (1 − γ ) C Nt−L .
F Nt+m = S Nt + mT N + C Nt-L + 1 + ((m−1) mod L) The conduct of the elderly inhabitant was characterized as regular or anomaly, depending on the equations derived. For the forecasting, the present activity duration was correlated with the forecasted duration. The confidence level of 95% was presumed for the prediction analysis. The acceptable limit of the period for any routine action was defined by Equation (21) below. If the actual duration was beyond the limit according to Equation (21), then an anomaly flag was triggered.

Classification and Performance Assessment
For classification issues, the methodology measures the implementation of a model as far as its error rate-level of erroneously classified occasions in the dataset. The present system manufactures a model since it needs to be utilized to arrange the new information. Henceforth, we are predominantly intrigued by model accuracy on new (concealed) information. We utilized two informational collections: the training set (seen information) to construct the model (decide its parameters), and the test set (concealed information) to measure its execution and performance (holding the parameters consistent). Usually, the system needs the validation set to tune the model. The validation set should not be utilized for testing (as it's not inconspicuous or hidden). Every one of the three informational datasets must be illustrative examples of the information that the model will be connected to.
The accessible informational dataset from all subjects are divided into 10 approximately equal size folds, and each fold generally has the same number of samples from every ADL of each subject. Seven folds are utilized as training information, one fold serves for cross-validation, and two-folds are for testing the model. Every one of the 10 folds is utilized precisely once as test information, and the test information is inconspicuous for the classifier. The outcomes revealed in the remainder of the paper are based on 10 test measures.
To validate the proposed algorithm, there are four possible results For ADL: 1. True positive (TP): The "A" activity happens and the algorithm detects it correctly the "A" activity.

2.
False positive (FP): The "A" activity does not occur and the algorithm reveals the "A" activity.

3.
True negative (TN): a daily event is performed and the algorithm does not detect it in the "A" activity.

4.
False negative (FN): The "A" activity happens but the algorithm does not detect it.

Experimental Analysis, Observations, and Comparative Arguments
Based on the experiments performed in four different elderly lone-living houses over the three hundred and one days, the ultimate inactive period and ultimate usage period of household objects could be calculated, through the provisional implementation duration of the Wellness platform. The trial duration is the function of an individual's well-being to perform ADL. Once the Wellness Time Series Model acquires the behavior of the day-to-day activities, at that point the provisional execution stage would be upgraded to the testing stage to evaluate the Wellness indices. Figures 8a and 8b represent Wellness level of four subject houses on a particular test day. Figure 8a shows the 16  nd Detection Methodology regivers diagnose and support only if the given information is number or well-being index. It is hard for a care provider to gh the position of deployment or node ID of sensory activation. e designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 e inactive interval of household stuff or events, whereas the Ϣ2 well as the active interval of household stuff or events. The Ϣ1 . Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly the BASIC indices, those were later upgraded through the of seasonal deviations and random days. The upgrading ve alert massages [6]. In the upgraded Wellness function, the been familiarized by the time series modeling. The below offered by dynamic sensor data fusion and time series principle.  Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
2 went below in subject house three; that day the elderly person slept more than usual because last night the elderly person had a visitor in the night, which disturbed their sleep. These explanations give an overview of dynamic Wellness determination and anomaly detection as a function of vel Wellness Indices Modelling and Detection Methodology ealthcare professionals and caregivers diagnose and support only if the given information is ser-friendly format, such as a number or well-being index. It is hard for a care provider to stand anything precisely through the position of deployment or node ID of sensory activation. r the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 sed on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 esigned through the usage as well as the active interval of household stuff or events. The Ϣ1 2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly tant was well. he functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the on of multiple observations of seasonal deviations and random days. The upgrading ably reduced the false positive alert massages [6]. In the upgraded Wellness function, the al deviation observations had been familiarized by the time series modeling. The below offered d in equations was introduced by dynamic sensor data fusion and time series principle. Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1. The performance characteristics of upgraded Wellness parameters examined through the sensory activations of 43 weeks from four households. Household-two recorded forty-four cautionary messages (highest among all four households) via an upgraded Wellness time series model. The cautionary messages comprise ultimate inactive use of a double-bed (7), ultimate inactive use of a dining armchair (10), ultimate inactive use of the sofa (10), and ultimate inactive application of the E & E household stuff or machinery (17).
The cause of several alarms was an ultimate inactive interval represented by Wellness function parameter (Basic) Ϣ1. The inhabitant was out of the home to visit relatives in the town and forgot to update the Wellness System. On the other hand, for the same situations, Upgraded Ϣ1, initiated less alert messages.
The Wellness index (Basic Ϣ2) from dining armchair, sofa, and double-bed caused twenty-seven alert messages. However, Wellness function (Upgraded Ϣ2) generated significantly less cautionary memos to double bed (3), dining armchair (5), and sofa (5). With the dynamic observation consideration of Wellness, the machine learning model attained a decrease in the alerts. Additionally, this advancement in Wellness time-series pushed the threshold limit to minimize the false positives (fake alerts). False positives reduced by 53.70%.
The investigation of upgraded Wellness function and assessment of Basic Wellness parameters have been shown in Table 3. The improvement in performance of anomaly detection is also a function of the sensor data fusion approach. For household 1 and 3, the Basic Wellness time series model shows an ultimate active period of taking food crossed the boundary and into inconsistency. Nevertheless, conferring to (Upgraded Ϣ2), the elderly had a friend for dinner. They were chatting during the meal and took extra time for the eating activity. In household 2, the was inconsistency detected by both Basic and upgraded Wellness time series; the elderly occupant was unhealthy and sleeping longer. Moreover, in another activity from elderly household 2, the Basic Wellness timeseries failed to recognize that the over-usage of the toilet was caused by the guest, and Upgraded Wellness time-series discovered the presence of a guest. Whereas for household 4 from the Table 3, it shows the irregularity and over-usage of the toilet from both Basic and Upgraded Wellness timeseries, since the sensing system did not record any guest or visitor. The following new annotations have been used: regular (RL), Anomaly (AL), subject house (Sub). Ϣ2 Basic* and Ϣ2 Upgraded* were evaluated exclusively when the Definite period was more than the ultimate period.   ndices Modelling and Detection Methodology fessionals and caregivers diagnose and support only if the given information is ormat, such as a number or well-being index. It is hard for a care provider to g precisely through the position of deployment or node ID of sensory activation. ssible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 sage as well as the inactive interval of household stuff or events, whereas the Ϣ2 gh the usage as well as the active interval of household stuff or events. The Ϣ1 bilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly .  Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an 2 for sleeping activity for four different houses up to one week.
The performance characteristics of upgraded Wellness parameters examined through the sensory activations of 43 weeks from four households. Household-two recorded forty-four cautionary messages (highest among all four households) via an upgraded Wellness time series model. The cautionary messages comprise ultimate inactive use of a double-bed (7), ultimate inactive use of a dining armchair (10), ultimate inactive use of the sofa (10), and ultimate inactive application of the E & E household stuff or machinery (17).
The cause of several alarms was an ultimate inactive interval represented by Wellness function parameter (Basic) delling and Detection Methodology s and caregivers diagnose and support only if the given information is ch as a number or well-being index. It is hard for a care provider to ly through the position of deployment or node ID of sensory activation. ution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 ell as the inactive interval of household stuff or events, whereas the Ϣ2 sage as well as the active interval of household stuff or events. The Ϣ1 nctions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly   Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an ess Indices Modelling and Detection Methodology professionals and caregivers diagnose and support only if the given information is ly format, such as a number or well-being index. It is hard for a care provider to thing precisely through the position of deployment or node ID of sensory activation. t possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 on-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 hrough the usage as well as the active interval of household stuff or events. The Ϣ1 robabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly well. ions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the ultiple observations of seasonal deviations and random days. The upgrading uced the false positive alert massages [6]. In the upgraded Wellness function, the ion observations had been familiarized by the time series modeling. The below offered tions was introduced by dynamic sensor data fusion and time series principle.
being function was formulated over the inactive spell duration of household use by abitant; t was the definite spell duration of idleness (inactive) of all or specific was ultimate-idleness interval and outside this may grounds abnormality situation as calculated from past observations. For the regular life of an individual Ϣ1 should ness function was formulated over the active spell duration of household use by the ant; TA was the definite spell duration of activity of all or specific household; TN was ive interval and outside this may be the grounds for an abnormality situation. That alculated from past observations. For the regular life of an individual, Ϣ2 should be 1. ty is only calculated when TA > TN. The regular duration of household use of an 2 ) from dining armchair, sofa, and double-bed caused twenty-seven alert messages. However, Wellness function (Upgraded Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
2 ) generated significantly less cautionary memos to double bed (3), dining armchair (5), and sofa (5). With the dynamic observation consideration of Wellness, the machine learning model attained a decrease in the alerts. Additionally, this advancement in Wellness time-series pushed the threshold limit to minimize the false positives (fake alerts). False positives reduced by 53.70%.
The investigation of upgraded Wellness function and assessment of Basic Wellness parameters have been shown in Table 3. The improvement in performance of anomaly detection is also a function of the sensor data fusion approach. For household 1 and 3, the Basic Wellness time series model shows an ultimate active period of taking food crossed the boundary and into inconsistency. Nevertheless, conferring to (Upgraded ices Modelling and Detection Methodology ssionals and caregivers diagnose and support only if the given information is mat, such as a number or well-being index. It is hard for a care provider to precisely through the position of deployment or node ID of sensory activation. ible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 ge as well as the inactive interval of household stuff or events, whereas the Ϣ2 h the usage as well as the active interval of household stuff or events. The Ϣ1 listic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly 1 and Ϣ2 were the BASIC indices, those were later upgraded through the e observations of seasonal deviations and random days. The upgrading the false positive alert massages [6]. In the upgraded Wellness function, the servations had been familiarized by the time series modeling. The below offered as introduced by dynamic sensor data fusion and time series principle. function was formulated over the inactive spell duration of household use by t; t was the definite spell duration of idleness (inactive) of all or specific ltimate-idleness interval and outside this may grounds abnormality situation culated from past observations. For the regular life of an individual Ϣ1 should 2 ), the elderly had a friend for dinner. They were chatting during the meal and took extra time for the eating activity. In household 2, the was inconsistency detected by both Basic and upgraded Wellness time series; the elderly occupant was unhealthy and sleeping longer. Moreover, in another activity from elderly household 2, the Basic Wellness time-series failed to recognize that the over-usage of the toilet was caused by the guest, and Upgraded Wellness time-series discovered the presence of a guest. Whereas for household 4 from the Table 3, it shows the irregularity and over-usage of the toilet from both Basic and Upgraded Wellness time-series, since the sensing system did not record any guest or visitor. The following new annotations have been used: regular (RL), Anomaly (AL), subject house (Sub). dices Modelling and Detection Methodology essionals and caregivers diagnose and support only if the given information is rmat, such as a number or well-being index. It is hard for a care provider to precisely through the position of deployment or node ID of sensory activation. sible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 age as well as the inactive interval of household stuff or events, whereas the Ϣ2 h the usage as well as the active interval of household stuff or events. The Ϣ1 ilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly Lunch Cooking ensor data fusion has been performed to recognize the sub-activity and ADL by Wellness ty modeling. Table 2 shows the sensor activation collected in the server laptop for four different ly houses, and the number of ADL recognized successfully from it. ovel Wellness Indices Modelling and Detection Methodology ealthcare professionals and caregivers diagnose and support only if the given information is ser-friendly format, such as a number or well-being index. It is hard for a care provider to rstand anything precisely through the position of deployment or node ID of sensory activation. fer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 ased on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 esigned through the usage as well as the active interval of household stuff or events. The Ϣ1 2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly itant was well. he functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the sion of multiple observations of seasonal deviations and random days. The upgrading rkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the nal deviation observations had been familiarized by the time series modeling. The below offered

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.  Table 4 shows the values of TIN and TN; the values were measured via derived equations. TIN and TN values lead to Wellness grades and remarks. A one thumb rule had been adopted in the present Smart Aging system-whenever an anomaly detected a sub-activity or activity that did not trigger an alarm, please check that event with multi-sensor data fusion to avoid a false positive. From the observation of the above table records, it is detected that only subject house 4 was free from an anomaly; on the other hand, the rest of the subject houses had anomaly forecasting tags. From subject 1 and 2, they had over-usage of toilet activities, but both had different causes of an anomaly. In the case of subject 1, it was upset stomach, whereas subject 2 had a visitor at home. Additionally, due to the visitor, subject 2 was sitting for more time in a dining armchair while eating, and that caused the anomaly of the armchair over-usage. Subject 3 was in the unhealthy condition that caused him to sleep a lot and eat less. Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an Sensor data fusion has been performed to recognize the sub-activity and ADL by Wellness activity modeling. Table 2 shows the sensor activation collected in the server laptop for four different elderly houses, and the number of ADL recognized successfully from it.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle. where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1. where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1.
The abnormality is only calculated when TA > TN. The regular duration of household use of an

Novel Wellness Indices Modelling and Detection Methodology
Healthcare professionals and caregivers diagnose and support only if the given information is in a user-friendly format, such as a number or well-being index. It is hard for a care provider to understand anything precisely through the position of deployment or node ID of sensory activation. To offer the best possible solution, we designed Wellness grades, Ϣ1 and Ϣ2. The formulation of Ϣ1 was based on non-usage as well as the inactive interval of household stuff or events, whereas the Ϣ2 was designed through the usage as well as the active interval of household stuff or events. The Ϣ1 and Ϣ2 were probabilistic functions. Therefore, when Ϣ1 and Ϣ2 were equivalent to 1, the elderly inhabitant was well.
The functions Ϣ1 and Ϣ2 were the BASIC indices, those were later upgraded through the inclusion of multiple observations of seasonal deviations and random days. The upgrading remarkably reduced the false positive alert massages [6]. In the upgraded Wellness function, the seasonal deviation observations had been familiarized by the time series modeling. The below offered method in equations was introduced by dynamic sensor data fusion and time series principle.
where Ϣ1 well-being function was formulated over the inactive spell duration of household use by the elderly inhabitant; t was the definite spell duration of idleness (inactive) of all or specific household; TIN was ultimate-idleness interval and outside this may grounds abnormality situation that duration was calculated from past observations. For the regular life of an individual Ϣ1 should be 1.
where Ϣ2 Wellness function was formulated over the active spell duration of household use by the elderly inhabitant; TA was the definite spell duration of activity of all or specific household; TN was an ultimate-active interval and outside this may be the grounds for an abnormality situation. That duration was calculated from past observations. For the regular life of an individual, Ϣ2 should be 1. The performance evaluation of the modeled novel Wellness classifier is recorded through the confusion matrix. Table 5 gives the annotation of ADL's. For the performance evaluation of the implemented system, a total of fourteen activities have been chosen and tested. B3 is the exit activity and the performance evaluation parameters for it are as follows: Sensitivity (0.9891), Specificity (0.9991), Precision (0.9891), Negative Predictive Value (0.9991), False Positive sensory-unit-based activities do not require the sensor data fusion and other sub-module for the activity recognition. On the other hand, the relaxing and eating activities have recorded the poorest score in those 14 activities, because these two activities use the sensory input of force sensors. Force sensors sometimes went in saturation when deployed over the Dining chair, Table, and Sofa set.
The overall scores for the selected 14 activities are as follows: Sensitivity (0.9852), Specificity (0.9988), Precision (0.9887), Accuracy (0.9974), F1 score (0.9851), Correlation Coefficient (0.9144), False Negative Rate (0.0130) A web-based Intelligent Aging framework had been produced. The data was gathered and treated through sensor data fusion calculations for the decision making. At last, data was transferred to the web link; the data over the internet was opened to a validated client by means of enlisted email id and secret word. The initial ADL recognition was completed by the heterogeneous sensor network; the push button indicator data did not apply. Nonetheless, there are a couple of exercises that the framework does not recognize effectively, for example, taking supper or pharmaceuticals on time. For this sort of exercises, either of the frameworks utilize prominent checking routes, for example, camera, wearable sensors, or go with a parental figure every minute of every day, which isn't attainable and economical [4,5]. Moreover, in the current investigation, the push button indicator information was applied to crosscheck the ADLs produced via the information secured from diverse sensing units. Figure 9a,b indicate previews in the Wellness framework website. This site comprises the information of recent ADLs. In order to check the observing historical ADLs from a specific date, the subject needs to choose a date and time. Figure 9a demonstrates the data on the non-electrical machine use. It demonstrates resting, eating, and latrine exercises: for instance, on August 30, a tenant went to the toilet in the morning for 00:10:43 (hours:minute:seconds). Figure 9b displays the observing of nourishment and medication of a tenant. It shows that the inhabitant was taking a drug soon after the food and daily three events of medication were recorded. A web-based Intelligent Aging framework had been produced. The data was gathered and treated through sensor data fusion calculations for the decision making. At last, data was transferred to the web link; the data over the internet was opened to a validated client by means of enlisted email id and secret word. The initial ADL recognition was completed by the heterogeneous sensor network; the push button indicator data did not apply. Nonetheless, there are a couple of exercises that the framework does not recognize effectively, for example, taking supper or pharmaceuticals on time. For this sort of exercises, either of the frameworks utilize prominent checking routes, for example, camera, wearable sensors, or go with a parental figure every minute of every day, which isn't attainable and economical [4,5]. Moreover, in the current investigation, the push button indicator information was applied to crosscheck the ADLs produced via the information secured from diverse sensing units. Figure 9A and Figure 9B indicate previews in the Wellness framework website. This site comprises the information of recent ADLs. In order to check the observing historical ADLs from a specific date, the subject needs to choose a date and time. Figure 9a demonstrates the data on the non-electrical machine use. It demonstrates resting, eating, and latrine exercises: for instance, on August 30, a tenant went to the toilet in the morning for 00:10:43 (hours:minute:seconds). Figure 9b displays the observing of nourishment and medication of a tenant. It shows that the inhabitant was taking a drug soon after the food and daily three events of medication were recorded.  Table 6 presented the comparative overview of the Wellness model with other most significant, recent, and peer-reviewed research works. The performance of the existing system is either average  Table 6 presented the comparative overview of the Wellness model with other most significant, recent, and peer-reviewed research works. The performance of the existing system is either average or very limited as compared to our offered research work. There are the following vital considerations and methodologies with support from the optimum Wellness system performance: 1.
The Wellness Model is an innovative methodology that classifies events inside definite temporal thresholds of activity initiation. The Wellness system recommended an accumulative overlapped-defined sized sliding window procedure, which sections the flooding sensor sub-activities into adaptable interval distances to assist the discovery of activities in an appropriate approach. Events are categorized as either prompt or periodical; an event based attitude can then be implemented for recognition of discrete events. The Wellness system was cross verified and examined by the subject´s inputs.

2.
Current research work accumulates sensing data, modeled ADLs, mines appropriate arithmetical features, and employs supervised machine learning to forecast well-being scores for healthcare applications.

3.
We offered dynamic frequent feature extraction and a forecast model based on Wellness indices time series modeling. Preliminary trials presented that one-day timespan was best for data mining, however, we constructed the framework to function on any dramatic time. Test outcomes have confirmed the usability of the research methodology to appropriately distinguish various household object, appliance usage, and offer long or short term trends accordingly.

Conclusions and Future Work
The well-being recognition performed by producing the ADLs was based on the movement inside the home and heterogeneous object usage. The smart Aging system applied the sensor data fusion approach to improve the anomaly detection over the large heterogeneous sensory data. These ADL recognition and well-being pattern generations distinguished any peculiarity change in the routine. Through Advanced believe model the mysterious and flawed behavior of sensing data was recognized, the model addressed the underfitting and overfitting. Wellness sensor data fusion model used sensor data from multiple contexts, and processed it on the basis of priority and event. The present Novel Wellness Indices Modeling and Detection Methodology processed the behavioral pattern from multiple sensor modalities with the least fake detection rate. Anomaly detection and forecasting methodology considered the cyclic variations and random day occasions. The Wellness Model processes data sampled at diverse rates and multi-modality sensory data with minimal and ordinal standards structured in hierarchies, not like numerous machine learning procedures that deal only with numeric entities.
Concerning pattern inconsistency, the assessment revealed that the Wellness model finds sensible, manifold, and distinct-length temporal values for every event. The benefit of Wellness based AAL is that it picks up the period from the timestamp in a solo scan uninterruptedly from the data stream of annotations without the requirement to reinstruct at what time fresh annotations developed. This distinctive merit permits it to include context-aware deviations for every trend as they are cultured and to adjust to variations over interval without reorientation. Wellness smart aging system presented that without including contextual features, the trends fail to reveal the ADL fluctuations. In such cases, irregularities can be discovered that don't relate to genuine deviations of the ordinary everyday practice. Wellness model defines contextual feature statistics during the analysis and learning stages, discovering deviations in routine and routine flexibility for every contextual feature. Context-aware trends and sequences could intimate dissimilarities in ADLs and enhance learning.
The framework was executed in four distinct households; two of them were exceptionally timeworn households. The framework had been outlined in a way that the elderly person was free from serious maintenance except for the power supply and physical damage. The smart aging system offered the average anomaly detection rate for 300 days and four elderly houses at 98.17%. The performance of evolution parameters for the selected 14 activities were as follows: Sensitivity (0.9852), Specificity (0.9988), Precision (0.9887), Accuracy (0.9974), F1 score (0.9851), Correlation Coefficient (0.9144), False Negative Rate (0.0130). Accuracy does not give an overall picture of the F1 score.
One of the major reasons behind the degradation in the F1 measure is noise and interference error. There is a need to filter data noises prior to the lifestyle modeling process. Two types of data noises should be filtered. First, anomaly behavioral patterns that are generated by the visitor. Second, anomaly sensor activations (data outlier) that are generated by the faulty sensor. To filter the former, CFM (Collaborative Filtering Model) with spatial states Gaussian Distribution (GD) would be a good methodology to discover routine behavioral patterns generated by the single resident, apart from visitors and pets. The assessment of the execution of frameworks on these smart aging datasets and the variety in their execution obviously underlines the significance of having the highest quality level which truly accomplishes the best quality, i.e., which are free of mistakes. This is by inferring hidden latent states that represent the resident's identity and habit. To filter the latter, the Advanced Belief Model is employed to filter data outliers from a faulty sensor, but it is not sufficient. We henceforth see this work as a first venturing stone in a bigger plan relating to enhancing the appraisal of the execution of a Smart Aging system.
Processed data was uploaded to the website with the help of the internet, and decision making information was produced. The uploaded data and decision-making information are accessible through the authentic login ID and password by the caregiver, healthcare professional, police, or any other official. In a few fields, those specifically related with the components of wellness (including wellbeing, movement, and emotion), some future examinations among experts (e.g., health specialists, medical attendants, analysts, and so on.) will profit from further enhancements of this model. Concerning current innovation and our past tasks, there is solid proof that the bits of knowledge and arrangements introduced are conceivable. The measurements exhibited have observable commitments to the prosperity of everybody, and we unequivocally trust those future activities established on this model will permit enhancement of the personal satisfaction of the elderly. A few perspectives were discussed with doctors, which presented to us a few viewpoints to represent in future executions. Besides, the elements of wellbeing and human-information technology communication strategies proposed are expected for the execution of "progressively characteristic" interfaces, which can adequately improve the well-being of the older adults.
For future work, we are intending to upgrade the Wellness indices model and present distributed learning in large data processing from different houses in near real time. This will help well-being applications to speedily take activities, for example, sending an alarm to patients or care suppliers. Besides, we are intending to construct a wellbeing cosmology model to naturally outline machines for potential ADL. This implies that we can proficiently prepare the framework and precisely distinguish human ADL.
Summary Points: (1) What is Already Known