A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults
Abstract
:1. Introduction
2. Materials and Methods
2.1. Conceptual Model
- (a)
- Using the IoT protocols, all the data information, such as localization and sensor data through embedded IoT devices in the user’s home environment, could be transmitted to the server and stored in the database. Caregivers or physicians can also override the reminder and subscribe to each particular topic if needed.
- (b)
- The MQTT protocol is based on the concept of bridging, which is available in some current MQTT broker implementations (such as Mosquitto, HiveMQ, and CloudMQTT). It connects a broker A to another broker B as a standard client, subscribing to all or a subset of the topics transmitted by clients to B. Since our task is to obtain data from the embedded devices and send notifications to the user’s AR user interface, we chose the MQTT broker HiveMQ (hivemq.com). This is a free cloud broker, which allows IoT devices to be connected to the cloud. By creating functions for each variable, we were able to separate the variables into different MQTT topics.
- (c)
- Once an event occurs in a particular home location, all the pertinent data values are checked on the server. Therefore, after the decision-making process on the cloud, data values will be updated. The caregiver and the older adults receive appropriate notifications.
- (d)
- An appropriate AR message will be sent to the user when the user interacts with a particular object (for example, when the user is looking for their medication) or when the sensor and localization tag detect an event (for example, when the user enters a dangerous zone at home). These messages are defined based on the decision of the fuzzy inference and the output values. The end user puts on an indoor positioning tag to monitor the real-time location of the user and make easy interaction with the home objects. The real-time position and orientation data values also form part of the decision-making engine’s inputs. Moreover, user activity patterns can be generated and stored in the database for further analysis.
- (e)
- The AR messages are sent to the user’s AR device while interacting with selected objects, or an event is detected from the sensor and anchors data. These messages are the output of the decision-making engine included in the service-based application.
2.2. Sample Scenarios
- The user may skip an activity, such as having a shower or eating breakfast, because they fail to recall it.
- The user often repeats activities because they do not remember what they already did, such as taking their medication.
- The user may forget to complete a task correctly or avoids a critical activity, such as turning off the stove or closing the main entrance.
2.3. System Architecture
2.4. Embedded Devices
2.5. Object-Based Decision-Making Process
2.5.1. Fuzzy Logic Implementation
2.5.2. Input/Output Variables
2.5.3. Fuzzy Rule Base
- Leaving home: If (rain status is Yes) and (distance from the main entrance is Near) and (heading angle is Small), then (image message is picture 3) and (voice message is audio 3).
- Cooking: If (distance from the refrigerator is Near) and (heading angle is Small), then (image message is picture 2) and (voice message is audio 2).
- Daily activity reminder: If (the plant’s humidity is Very dry), then (text message number is text 1).
- Daily activity reminder: If (distance from the TV is Near) and (heading angle is Small), then (image message is picture 6) and (voice message is audio 6).
- Cooking: If (time is Late Afternoon) and (distance from the oven is Near) and (heading angle is Small), then (image message is picture 4) and (voice message is audio 4).
- Medication reminder: If (time is Evening), then (image message is picture 5) and (voice message is audio 5).
- Alarm: If (flame sensor status is Yes), then (audio message is audio 7) and (relay status is Yes).
- Alarm: If (gas sensor status is Yes), then (text message is text 2).and (relay status is Yes).
- High Temperature Reminder: If (temperature sensor status is Hot), then (text message is text 3).
- Low Temperature Reminder: If (temperature sensor status is Cold), then (text message is text 4).
- Danger zone: If (distance from the fireplace is Near), then (voice message is audio 8).
- Danger zone: If (distance from the balcony is Near) and (heading angle is Small), then (image message is picture 9) and (voice message is audio 9).
2.6. User Indoor Location Identification
2.7. Augmented Reality Application
3. Results and Experimental Setup
3.1. Experiment A: Danger Zones and Reminders
3.2. Experiment B: Alerting Based on User’s Location and Sensors’ Data Values
3.3. Experiment C: Network Latency and System Response Time
4. Discussion
- Safety: One of the main challenges for the caregivers is to keep the person with memory impairment monitored. Caregivers cannot continuously watch the person, so an assistive system can benefit this case. The person can enter dangerous areas (for example, the balcony), fall, or even leave their home. In our experimental condition (Experiment and Setup A), we simulated this scenario and assessed the usability of the system in detecting dangerous areas and sending reminders to the user. The caregiver can also monitor these events.
- Personal assistance: Psychologists indicate that the engagement of people with memory impairment in routine activities is essential. An assistive tool with some functionalities of a personal assistant could be beneficial, mainly because people with MCI have problems with short-term memory and recent events. Thus, in Experiment and Setup B, we evaluated the system’s performance in sending correct reminders or turning off proper actuators to help the user complete an ongoing task based on the activated fuzzy rule. The system should perform correctly according to the object-based decision-making algorithm.
- Quick response: When people with memory impairment forget the place of an object or cannot complete an activity, they should be reminded how to make a correct decision. Otherwise, the person might be anxious or disappointed. In this regard, the AAL system should send reminders and alerts in real time. Thus, in the last experiment (Experiment and Setup C), we evaluated the system’s response time in sending such a message.
- The wearable devices, for example, localization tags and AR glasses, are required to be lightweight, small, consume lower power, and produce less heat, leading to scalability problems. As interesting as AR may look, some people may not be comfortable with wearing head-mounted displays all day. To overcome this challenge, we suggested lightweight AR glasses as an interaction device for the end user because of its lightweight and minimal heads-up screen, so that each person with MCI could potentially use it.
- In any AR application, it is necessary to be synchronized in real time for giving the user precise information. The device requires high bandwidth and the fastest data communication to keep the real-world and virtual content in sync. In this regard, the network latency in our proposed model should be satisfactory, and data loss should not occur after the execution of a series of data transfers.
- In some cases, AR can violate the user’s privacy and start saving personal preferences and information. In the proposed model, the collected data can only be shared with physicians or caregivers to monitor memory impairment progression and treatment response. Finally, individuals with MCI can control personal data based on their impairment stage.
5. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Lisko, I.; Kulmala, J.; Annetorp, M.; Ngandu, T.; Mangialasche, F.; Kivipelto, M. How Can Dementia and Disability Be Prevented in Older Adults: Where Are We Today and Where Are We Going? J. Intern. Med. 2021, 289, 807–830. [Google Scholar] [CrossRef] [PubMed]
- Bell, V.; Troxel, D. A Dignified Life: The Best FriendsTM Approach to Alzheimer’s Care: A Guide for Care Partners; Health Communications, Inc.: Sydney, Australia, 2012; ISBN 0757316654. [Google Scholar]
- Hayhurst, J. How Augmented Reality and Virtual Reality Is Being Used to Support People Living with Dementia—Design Challenges and Future Directions. In Augmented Reality and Virtual Reality; Springer: Berlin/Heidelberg, Germany, 2018; pp. 295–305. [Google Scholar] [CrossRef]
- Ghorbani, F.; Farshi Taghavi, M.; Delrobaei, M. Towards an Intelligent Assistive System Based on Augmented Reality and Serious Games. Entertain. Comput. 2022, 40, 100458. [Google Scholar] [CrossRef]
- Koulouris, D.; Menychtas, A.; Maglogiannis, I. An IoT-Enabled Platform for the Assessment of Physical and Mental Activities Utilizing Augmented Reality Exergaming. Sensors 2022, 22, 3181. [Google Scholar] [CrossRef]
- Hossain, M.S.; Hardy, S.; Alamri, A.; Alelaiwi, A.; Hardy, V.; Wilhelm, C. AR-Based Serious Game Framework for Post-Stroke Rehabilitation. Multimed. Syst. 2016, 22, 659–674. [Google Scholar] [CrossRef]
- Chang, Y.J.; Kang, Y.S.; Huang, P.C. An Augmented Reality (AR)-Based Vocational Task Prompting System for People with Cognitive Impairments. Res. Dev. Disabil. 2013, 34, 3049–3056. [Google Scholar] [CrossRef]
- Guerrero, E.; Lu, M.H.; Yueh, H.P.; Lindgren, H. Designing and Evaluating an Intelligent Augmented Reality System for Assisting Older Adults’ Medication Management. Cogn. Syst. Res. 2019, 58, 278–291. [Google Scholar] [CrossRef]
- Kanno, K.M.; Lamounier, E.A.; Cardoso, A.; Lopes, E.J.; Fakhouri Filho, S.A. Assisting Individuals with Alzheimer’s Disease Using Mobile Augmented Reality with Voice Interaction: An Acceptance Experiment with Individuals in the Early Stages. Res. Biomed. Eng. 2019, 35, 223–234. [Google Scholar] [CrossRef]
- Haidon, C.; Pigot, H.; Giroux, S. Joining Semantic and Augmented Reality to Design Smart Homes for Assistance. J. Rehabil. Assist. Technol. Eng. 2020, 7, 205566832096412. [Google Scholar] [CrossRef]
- Ben Hassen, H.; Dghais, W.; Hamdi, B. An E-Health System for Monitoring Elderly Health Based on Internet of Things and Fog Computing. Health Inf. Sci. Syst. 2019, 7, 24. [Google Scholar] [CrossRef]
- Hussain, M.; Zaidan, A.A.; Zidan, B.B.; Iqbal, S.; Ahmed, M.M.; Albahri, O.S.; Albahri, A.S. Conceptual Framework for the Security of Mobile Health Applications on Android Platform. Telemat. Inform. 2018, 35, 1335–1354. [Google Scholar] [CrossRef]
- Cornet, V.P.; Holden, R.J. Systematic Review of Smartphone-Based Passive Sensing for Health and Wellbeing. J. Biomed. Inform. 2018, 77, 120–132. [Google Scholar] [CrossRef]
- Wang, L.; Tang, D.; Liu, C.; Nie, Q.; Wang, Z.; Zhang, L. An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing. Sensors 2022, 22, 6472. [Google Scholar] [CrossRef]
- Brunete, A.; Gambao, E.; Hernando, M.; Cedazo, R. Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments. Sensors 2021, 21, 2212. [Google Scholar] [CrossRef]
- Perez, A.J.; Siddiqui, F.; Zeadally, S.; Lane, D. A Review of IoT Systems to Enable Independence for the Elderly and Disabled Individuals. Internet Things 2022, 21, 100653. [Google Scholar] [CrossRef]
- Tun, S.Y.Y.; Madanian, S.; Mirza, F. Internet of Things (IoT) Applications for Elderly Care: A Reflective Review. Aging Clin. Exp. Res. 2021, 33, 855–867. [Google Scholar] [CrossRef] [PubMed]
- Cedillo, P.; Sanchez, C.; Campos, K.; Bermeo, A. A Systematic Literature Review on Devices and Systems for Ambient Assisted Living: Solutions and Trends from Different User Perspectives. In Proceedings of the 2018 5th IEEE International Conference on eDemocracy and eGovernment, ICEDEG 2018, Ambato, Ecuador, 4–6 April 2018; pp. 59–66. [Google Scholar]
- Vijayalakshmi, A.; Jose, D.V. Internet of Things for Ambient-Assisted Living—An Overview. In Internet of Things Use Cases for the Healthcare Industry; Springer: Berlin/Heidelberg, Germany, 2020; pp. 221–239. [Google Scholar]
- Pandya, B.; Shah, D.; Pourabdollah, A.; Lotfi, A. Developing and Comparing Cloud-Based Fuzzy Systems for Monitoring Health Related Signals in Assistive Environments. In Proceedings of the ACM International Conference Proceeding Series, New York, NY, USA, 1–3 September 2021; pp. 407–413. [Google Scholar]
- Mufti, T.; Sohail, S.S.; Gupta, B.; Agarwal, P. Sustainable Approach for Cloud-Based Framework Using IoT in Healthcare. In Smart Technologies for Energy and Environmental Sustainability; Springer: Berlin/Heidelberg, Germany, 2022; pp. 231–244. [Google Scholar]
- Ngankam, H.K.; Pigot, H.; Giroux, S. OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes. Electronics 2022, 11, 1143. [Google Scholar] [CrossRef]
- Khan, N.J.; Ahamad, G.; Naseem, M.; Khan, Q.R. Fuzzy Discrete Event System (FDES): A Survey. In Renewable Power for Sustainable Growth, Proceedings of International Conference on Renewal Power (ICRP 2020), Jammu, India, 17–18 April 2020; Springer: Berlin/Heidelberg, Germany, 2021; pp. 531–544. [Google Scholar]
- Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, M. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347–2376. [Google Scholar] [CrossRef]
- Kothig, A.; Muñoz, J.; Mahdi, H.; Aroyo, A.M.; Dautenhahn, K. HRI Physio Lib: A Software Framework to Support the Integration of Physiological Adaptation in HRI. In Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ICSR 2020, Golden, CO, USA, 14–18 November 2020; Springer: Berlin/Heidelberg, Germany, 2020; Volume 12483 LNAI, pp. 36–47. [Google Scholar]
- Lotfi, A.; Langensiepen, C.; Mahmoud, S.M.; Akhlaghinia, M.J. Smart Homes for the Elderly Dementia Sufferers: Identification and Prediction of Abnormal Behaviour. J. Ambient Intell. Humaniz. Comput. 2012, 3, 205–218. [Google Scholar] [CrossRef]
- Liu, C.-T.; Chan, C.-T. A Fuzzy Logic Prompting Mechanism Based on Pattern Recognition and Accumulated Activity Effective Index Using a Smartphone Embedded Sensor. Sensors 2016, 16, 1322. [Google Scholar] [CrossRef] [Green Version]
- Kaur, J.; Kaur, K. A Fuzzy Approach for an IoT-Based Automated Employee Performance Appraisal. Comput. Mater. Contin. 2017, 53, 24–38. [Google Scholar]
- Mohmed, G.; Lotfi, A.; Pourabdollah, A. Enhanced Fuzzy Finite State Machine for Human Activity Modelling and Recognition. J. Ambient Intell. Humaniz. Comput. 2020, 11, 6077–6091. [Google Scholar] [CrossRef]
- Jindal, A.; Dua, A.; Kumar, N.; Das, A.K.; Vasilakos, A.V.; Rodrigues, J.J.P.C. Providing Healthcare-as-a-Service Using Fuzzy Rule Based Big Data Analytics in Cloud Computing. IEEE J. Biomed. Health Inform. 2018, 22, 1605–1618. [Google Scholar] [CrossRef]
- Boukerche, A.; Kantarci, B.; Kaptan, C. Towards Ensuring the Reliability and Dependability of Vehicular Crowd-Sensing Data in GPS-Less Location Tracking. Pervasive Mob. Comput. 2020, 68, 101248. [Google Scholar] [CrossRef]
- Kok, M.; Schon, T.B. Magnetometer Calibration Using Inertial Sensors. IEEE Sens. J. 2016, 16, 5679–5689. [Google Scholar] [CrossRef] [Green Version]
- Madgwick, S.O.H.; Harrison, A.J.L.; Vaidyanathan, R. Estimation of IMU and MARG Orientation Using a Gradient Descent Algorithm. In Proceedings of the IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland, 29 June–1 July 2011; pp. 1–7. [Google Scholar]
- Verma, P.; Sood, S.K. Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes. IEEE Internet Things J. 2018, 5, 1789–1796. [Google Scholar] [CrossRef]
- Oguntala, G.; Abd-Alhameed, R.; Jones, S.; Noras, J.; Patwary, M.; Rodriguez, J. Indoor Location Identification Technologies for Real-Time IoT-Based Applications: An Inclusive Survey. Comput. Sci. Rev. 2018, 30, 55–79. [Google Scholar] [CrossRef]
- Molisch, A.F.; Balakrishnan, K.; Chong, C.; Emami, S.; Fort, A.; Karedal, J.; Kunisch, J.; Schantz, H.; Schuster, U.; Siwiak, K. IEEE 802. 15. 4a Channel Model—Final Report. IEEE P802 2004, 15, 0662. [Google Scholar]
- Kalantar, S.; Zimmer, U.R. Optima Localization by Vehicle Formations Imitating the Nelder-Mead Simplex Algorithm. Auton. Robots 2009, 27, 239–260. [Google Scholar] [CrossRef]
- Linowes, J.; Babinlinski, K. Augmented Reality for Developer: Build Practical Augmented Reality Applications with UNITY, ARCore, ARKit, and Vuforia; Packt Publishing Ltd.: Birmingham, UK, 2017; Volume 433–435, ISBN 1787286436. [Google Scholar]
- Ramadhan, J.M.; Mardiati, R.; Haq, I.N. Iot Monitoring System for Solar Power Plant Based on Mqtt Publisher/Subscriber Protocol. In Proceedings of the 2021 7th IEEE International Conference on Wireless and Telematics (ICWT), Bandung, Indonesia, 19–20 August 2021; pp. 1–6. [Google Scholar]
- Cicirelli, G.; Marani, R.; Petitti, A.; Milella, A.; D’orazio, T. Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population. Sensors 2021, 21, 3549. [Google Scholar] [CrossRef]
- Mishra, A.; Karmakar, S.; Bose, A.; Dutta, A. Design and Development of IoT-Based Latency-Optimized Augmented Reality Framework in Home Automation and Telemetry for SMART lifestyle. J. Reliab. Intell. Environ. 2020, 6, 169–187. [Google Scholar] [CrossRef]
- Raj, P.; Chatterjee, J.M.; Kumar, A.; Balamurugan, B. Internet of Things Use Cases for the Healthcare Industry; Springer: Berlin/Heidelberg, Germany, 2020; ISBN 3030375269. [Google Scholar]
- Wagner, F.; Basran, J.; Dal Bello-Haas, V. A Review of Monitoring Technology for Use with Older Adults. J. Geriatr. Phys. Ther. 2012, 35, 28–34. [Google Scholar] [CrossRef] [PubMed]
- Vanneste, P.; Huang, Y.; Park, J.Y.; Cornillie, F.; Decloedt, B.; Van den Noortgate, W. Cognitive Support for Assembly Operations by Means of Augmented Reality: An Exploratory Study. Int. J. Hum. Comput. Stud. 2020, 143, 102480. [Google Scholar] [CrossRef]
- Lorusso, L.; Mosmondor, M.; Grguric, A.; Toccafondi, L.; D’Onofrio, G.; Russo, S.; Lampe, J.; Pihl, T.; Mayer, N.; Vignani, G. Design and Evaluation of Personalized Services to Foster Active Aging: The Experience of Technology Pre-Validation in Italian Pilots. Sensors 2023, 23, 797. [Google Scholar] [CrossRef]
- Chaparro, J.D.; Ruiz, J.F.-B.; Romero, M.J.S.; Peño, C.B.; Irurtia, L.U.; Perea, M.G.; del Toro Garcia, X.; Molina, F.J.V.; Grigoleit, S.; Lopez, J.C. The SHAPES Smart Mirror Approach for Independent Living, Healthy and Active Ageing. Sensors 2021, 21, 7938. [Google Scholar] [CrossRef]
- Žarić, N.; Radonjić, M.; Pavlićević, N.; Žarić, S.P. Design of a Kitchen-Monitoring and Decision-Making System to Support Aal Applications. Sensors 2021, 21, 4449. [Google Scholar] [CrossRef] [PubMed]
- Grgurić, A.; Mošmondor, M.; Huljenić, D. The Smarthabits: An Intelligent Privacy-Aware Home Care Assistance System. Sensors 2019, 19, 907. [Google Scholar] [CrossRef] [Green Version]
- Félix, J.; Moreira, J.; Santos, R.; Kontio, E.; Pinheiro, A.R.; Sousa, A.S.P. Health-Related Telemonitoring Parameters/Signals of Older Adults: An Umbrella Review. Sensors 2023, 23, 796. [Google Scholar] [CrossRef] [PubMed]
- Taramasco, C.; Rimassa, C.; Martinez, F. Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population. Sensors 2023, 23, 268. [Google Scholar] [CrossRef] [PubMed]
- Freytag, J.; Mishra, R.K.; Street, R.L.; Catic, A.; Dindo, L.; Kiefer, L.; Najafi, B.; Naik, A.D. Using Wearable Sensors to Measure Goal Achievement in Older Veterans with Dementia. Sensors 2022, 22, 9923. [Google Scholar] [CrossRef]
- Lo Bianco, M.; Pedell, S.; Renda, G. Augmented Reality and Home Modifications: A Tool to Empower Older Adults in Fall Prevention. In Proceedings of the 28th Australian Computer-Human Interaction Conference, OzCHI 2016, New York, NY, USA, 9 November–2 December 2016; pp. 499–507. [Google Scholar]
- Blattgerste, J.; Renner, P.; Pfeiffer, T. Augmented Reality Action Assistance and Learning for Cognitively Impaired People—A Systematic Literature Review. In Proceedings of the ACM International Conference Proceeding Series, New York, NY, USA, 1–3 September 2021; pp. 270–279. [Google Scholar]
- Ghorbani, F.; Kia, M.; Delrobaei, M.; Rahman, Q. Evaluating the Possibility of Integrating Augmented Reality and Internet of Things Technologies to Help Patients with Alzheimer’s Disease. In Proceedings of the 2019 26th IEEE National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, Tehran, Iran, 27–28 November 2019; pp. 139–144. [Google Scholar]
- Salichs, M.A.; Encinar, I.P.; Salichs, E.; Castro-González, Á.; Malfaz, M. Study of Scenarios and Technical Requirements of a Social Assistive Robot for Alzheimer’s Disease Patients and Their Caregivers. Int. J. Soc. Robot. 2016, 8, 85–102. [Google Scholar] [CrossRef]
- El Murabet, A.; Abtoy, A.; Touhafi, A.; Tahiri, A. Ambient Assisted Living System’s Models and Architectures: A Survey of the State of the Art. J. King Saud Univ. Comput. Inf. Sci. 2020, 32, 1–10. [Google Scholar] [CrossRef]
- Burigat, S.; Chittaro, L. Navigation in 3D Virtual Environments: Effects of User Experience and Location-Pointing Navigation Aids. Int. J. Hum. Comput. Stud. 2007, 65, 945–958. [Google Scholar] [CrossRef]
- Janani, S.K.; Swarnalatha, P. Study of Human–Computer Interaction in Augmented Reality. In Advances in Intelligent Systems and Computing; Springer: Berlin/Heidelberg, Germany, 2020; Volume 1057, pp. 835–846. ISBN 9789811501838. [Google Scholar]
Parameter | Fuzzy Membership Functions | Data Type | Direction |
---|---|---|---|
Heading angle | Large, medium, and small | Linguistic | Input |
Distance | Very far, far, near | Linguistic | Input |
Time | Midnight night evening, late afternoon early afternoon morning, early morning | Linguistic | Input |
Temperature | Very hot, hot, warm, mild, cool, cold, very cold | Linguistic | Input |
Humidity | Very humid, humid, dry, very dry | Linguistic | Input |
Rain detection | Yes, No | Boolean | Input |
Flame detection | Yes, No | Boolean | Input |
Gas detection | Yes, No | Boolean | Input |
Relay status | Yes, No | Boolean | Output |
Voice message | 1, 2, …, 20 | Integer | Output |
Image message | 1, 2, …, 20 | Integer | Output |
Text message | 1, 2, …, 10 | Integer | Output |
Object Number | Experiment Number | Distance (dm) | Heading Angle (degree) | Membership Function of Distance | Membership Function of Heading Angle | Activated Rule Number |
---|---|---|---|---|---|---|
1 | 1 2 3 4 | 3 4 8 14 | 12 35 10 16 | Near Near Far - | Small Medium Small Small | 12 - - - |
2 | 5 6 7 8 | 10 7 12 2 | 42 12 73 11 | Very Far Far Very Far Near | Medium Small Large Small | - - - 14 |
3 | 9 10 11 12 | 5 16 6 13 | 122 9 38 12 | Near - Far Very Far | - Small Medium Small | 11 - - - |
4 | 13 14 15 16 | 18 4 11 7 | 0 10 14 72 | - Near Very Far Far | Small Small Small Large | - 21 - - |
5 | 17 18 19 20 | 12 2 17 13 | 86 12 8 76 | Very Far Near - Very Far | Large Small Small Large | - 2 - - |
Experiment Number | User’s Location (x,y) | User’s Heading Angle (degree) | Location of Anchor1(A1) (x,y) | Location of Anchor2(A2) (x,y) | Location of Anchor3(A3) (x,y) | Location of an Object (x,y) |
---|---|---|---|---|---|---|
1 | (1,1) | 263 | (0,0) | (2.8,4.8) | (7.2,2.4) | (5,3) |
2 | (4.6,2.6) | 105 | (0,0) | (2.8,4.8) | (7.2,2.4) | (5,3) |
3 | (4.8,2.8) | 3 | (0,0) | (2.8,4.8) | (7.2,2.4) | (5,3) |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ghorbani, F.; Ahmadi, A.; Kia, M.; Rahman, Q.; Delrobaei, M. A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults. Sensors 2023, 23, 2673. https://doi.org/10.3390/s23052673
Ghorbani F, Ahmadi A, Kia M, Rahman Q, Delrobaei M. A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults. Sensors. 2023; 23(5):2673. https://doi.org/10.3390/s23052673
Chicago/Turabian StyleGhorbani, Fatemeh, Amirmasoud Ahmadi, Mohammad Kia, Quazi Rahman, and Mehdi Delrobaei. 2023. "A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults" Sensors 23, no. 5: 2673. https://doi.org/10.3390/s23052673