A Helping Hand to the Elderly: Securing Their Freedom through the HAIE Framework

Abstract

The life expectancy of the elderly has substantially increased compared to earlier times. The primary factors are greater awareness of nutrition, the environment, and personal hygiene. Advances in science and technology have also extended the lifespans of the aging population. Traditional care methods are inadequate to address this situation. To maintain the socioeconomic structure, there is a need for the integration of advanced frameworks. In this context, we propose a smart framework called human activity Internet of Things-enabled environment (HAIE) to provide a non-human assistive environment that helps the elderly live independently. Research into aging in place and assistive environments has focused on modernized environments, largely neglecting the impact of technology on the lives of elderly individuals who stay at home. This work addresses the gap by integrating advanced technologies, such as cloud computing and the Internet of Things (IoT). The inclusion of IoT facilitates a smart and automated environment for the elderly. Cloud integration enables the storage of large volumes of data for further analysis and the identification of patterns for future advancements. It also introduces the concept of accessing data from anywhere, on any device, at any time. To validate this work, two primary parameters were considered: accuracy and latency. Through simulation, the proposed HAIE model has demonstrated an accuracy of 93% out of 100 attempts and a latency of 84.35 ms for the deployed case studies under the HAIE framework.

1. Introduction

Aging in place (AiP) can be defined in several ways, one of which is the practice of older people staying in their homes and neighborhoods as they age, rather than relocating or entering a facility. Aging is a complex process influenced by various biological, social, and environmental factors. One of the primary biological causes of aging is the accumulation of cellular and molecular damage over time. This gradual buildup of damage can lead to the deterioration of physical and mental abilities, an increased risk of developing diseases, and ultimately death. However, these changes are not always linear or consistent, and they may not directly correspond to an individual’s chronological age. The biological changes in humans can also impact other aspects such as friend circles, living environments, etc. These social and environmental factors also play a role in shaping the experience of aging and contributing to age-related diversity [1]. Individuals have various reasons for choosing to spend their later years in their current homes, just as they may have different reasons for leaving and moving into a retirement community. However, many families must prioritize this issue due to mobility and health difficulties [2]. Most seniors will eventually require some form of assistance with daily tasks, whether in a communal environment or at home. The aging population is a global issue that refers to the increasing number and proportion of older people in the world’s population. This phenomenon is becoming more pronounced over time. As of 2019, there were an estimated 703 million people aged 65 or older worldwide. By 2050, this number is projected to increase to 1.5 billion. The proportion of older people in the global population has been steadily increasing over time. In 1990, individuals aged 65 and older accounted for 6% of the world’s population. By 2019, this percentage had risen to 9%. It is expected that by 2050, this proportion will further increase to 16%. This means that one in six people worldwide will be aged 65 or older [3]. Figure 1 represents the forecasted life expectancy of the elderly population until 2025. From Figure 1, it is evident that the elderly population is growing rapidly, requiring immediate attention from society. The proposed work aims to address this need and provide a sociotechnical solution.

1.1. Involvement of IoT and the Cloud in Smart Homes for the Elderly

The concept of the Internet of Things (IoT) involves the interconnection of various devices to the internet. These devices, such as sensors and actuators, are equipped with a telecommunication interface, a processing unit, limited storage, and software applications [5]. The IoT facilitates the integration of these objects into the internet, allowing interactions between people and devices, as well as between devices themselves [6]. The term IoT refers to the integration of diverse devices capable of sending and receiving signals. IoT deployment can be observed through sensors, near-field communication, guidance systems, DDPG [7], mobile devices, energy-harvesting cognitive networks [8], etc. These devices are connected to the internet to exchange information, significantly impacting social and environmental domains to achieve a shared objective [9].

The integration of IoT with computer intelligence is likely to be incorporated into the gadgets necessary for managing modern homes [10]. In recent years, several initial efforts have been made to design and implement smart homes using IoT technologies. The unified IoT-based smart home paradigm aims to connect all household devices and appliances [11]. In studies by the authors of [12,13], a smart home was created by networking smart objects using ZigBee technology and programming sensors and actuators using the Arduino platform. Furthermore, cloud computing has transformed home services and applications in the field of home automation. Manufacturers today integrate IoT with the cloud in various devices to meet modern demands. Cloud-based smart homes are technically viable and will have a significant societal impact. The virtualization of services allows customers to access the system and its services from anywhere, at any time, and on any device. This integration offers many benefits, such as security, reliability, scalability, and interoperability. Figure 2 illustrates the various applications made possible through IoT technology.

1.2. Proposed Objective with Assistive Home for Aging in Place

People who require assistance with daily living activities but desire freedom from constant human assistance can consider technology-assisted living facilities. The primary goal of this work is to provide an IoT and cloud-integrated smart framework for the elderly in their own homes. Through this model, the elderly can live comfortably with minimal human dependency. When residents enter a technology-assisted living facility, most of them will develop a comprehensive service plan that is regularly reviewed to ensure they receive appropriate care as their conditions change. These services include assistance with meal preparation, bathing, dressing, domestic tasks, and aid for those who experience disorientation or memory issues [14]. Many synonymical terms exist for the same model, such as residential care, personal care, adult communal living, and support care. Some of these services can be eliminated with the help of household control systems [15].

The proposed advanced smart home is a comprehensive system that incorporates several components, including the traditional smart home, the Internet of Things (IoT), cloud computing, and rule-based event processing. Each of these components contributes unique features to the suggested system. The presence of IoT establishes a network of sensor-based devices, enabling the monitoring of equipment performance and functionalities. The integration of computer intelligence into home appliances automates and optimizes various home functions based on user preferences and needs. Cloud computing facilitates the storage and processing of large amounts of data, which can be used to improve the efficiency of the smart home system. Rule-based event processing enables the detection and response to various events and triggers within the smart home system. Overall, the proposed advanced smart home system brings together various cutting-edge technologies to create a more efficient and user-friendly living environment [16]. Figure 3 illustrates the interconnection of all elements in an AiP configuration, including the aged person, multiple care networks, and assistive environment IoT technology systems.

A cloud-integrated IoT-based smart home addresses the challenges faced by the elderly living at home. It utilizes advanced technologies to monitor their daily behaviors. The aging-in-place issue can be addressed through smart technologies. The presence of the cloud enables the collection of data that is essential for behavior pattern analysis [17]. Figure 3 illustrates the character network theory (CNT) framework, which depicts the relationships and dependencies among the characters or stakeholders involved. These relationships influence the decision-making process. In this model, both living and non-living objects participate in data transactions. The cloud-based IoT-enabled smart home for AiP ensures proper care without human intervention. The cloud helps to monitor data from the remote end and enables control of smart technologies accordingly.

2. Literature Review

It is somewhat paradoxical that medical advancements have increased individuals’ lifespans. There is a need for strategies to ensure that those extra years are spent with a high quality of life [18]. The authors of [19] have proposed a Help to You (H2U)-based IoT system that aims to enhance the quality of life for elders through the use of wearable gadgets, biosensors, and wireless sensor networks-based environments. Considering that elderly individuals tend to spend most of their time at home, deploying sensor-assistive devices can significantly improve their quality of life. Information from systematic literature reviews can be used to create innovative applications that enhance the health and overall well-being of elders in their homes [20].

The findings indicate that ICT, specifically automatic video monitoring, audio analysis, and accelerometers, can be used to assess the autonomy of patients with dementia and mild cognitive impairment (MCI). This could provide diagnostically relevant information to clinicians, enabling real-time assessment while reducing observer bias. Therefore, to effectively monitor older individuals, certain IoT-based applications are deployed, such as Internet of Medical Things (IOMT) devices, feature-based implicit irregularity detection (FIID), wearable sensor(s) (WS) (including heartbeat sensors, body temperature sensors, room temperature sensors, CO and CO2 sensors, and autonomous sensor nodes), mobile robot system(s) (MRS), and video surveillance systems [21]. Planning and revising public health policies in society must consider the aging population. Under the banner “Aging Well in the Digital World”, initiatives created under the European Active Assisted Living program aim to keep individuals connected, healthy, and active as they age, providing them with a secure yet independent living environment (citation needed). Figure 4 illustrates the literature tree based on existing work. The main objective of IoT is the continuous monitoring and management of everyday items over the internet.

The robust system discussed in [22], called Not Alone at Home (NOAH), utilizes specialized home sensors to non-intrusively record various aspects of daily living activities. The authors of [23] proposed a block representation of an IoT-based home monitoring system (HMS). This system can be accessed remotely and it is suitable for both general and coronavirus disease (COVID) patients. The general use of monitoring temperature, blood oxygen levels, and heart rate, along with an in-built global positioning system (GPS) system to track COVID patients, is common. Interviews were conducted based on four criteria: perceived comfort, perceived usability, perceived privacy, and perceived benefit. These four criteria were used to identify the set of sensors needed for an integrated smart home system. The sensor set was then subjected to technological tests on nine older individuals who were living independently in a South Korean senior welfare institution, followed by two focus group interviews. According to the findings of this study, older participants evoked negative comments regarding the complexity of usability and discomfort during everyday activities, which is consistent with earlier studies. Despite these replies, the elderly acknowledged the requirement for the integrated smart home system. They displayed a high degree of desire after becoming sufficiently informed about its advantages [24]. The Internet of Things (IoT) has a wide range of uses, including home automation, automated machinery, agriculture, the financial industry, and smart cities. Urban residents are becoming much more prone to lifestyle diseases, and diagnosing and treating illnesses costs a lot of money. The use of IoT in the healthcare industry enables real-time patient monitoring, timely alarms for health examinations when necessary, and periodic information sharing. IoT-enabled services in the health sector are essential for patients since doctors often have to treat patients from remote locations during pandemic situations, such as the one we are currently experiencing with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [6]. The linked gadgets can aid in surveillance and disease control, as well as track dietary needs, mental well-being, self-care, and emergency services, leading to an efficient health management system. Alzheimer’s disease is the most common cause of dementia, which refers to a progressive decline in thinking, behavioral, and social skills that impairs a person’s ability to function independently [25]. Information and communication technology (ICT) may, however, overcome these limitations by better capturing the symptoms of Alzheimer’s disease. Researchers have looked into the use of various sensors, such as video monitoring systems or audio analysis, for evaluating individuals with dementia and mild cognitive impairment (MCI).

Sometimes, people worry about their elderly family members who live alone. These individuals conduct difficult tasks by themselves to survive, which can be difficult given their age. To address this issue, many IoT devices are being developed, such as the Raspberry Pi IoT device, specifically designed for elderly people. It has seven motion and temperature-humidity sensors, which are located at seven points within an elderly person’s home [26]. Because of the rapidly aging population and the attendant issues in health and social care, assistive living has become a focal point. Reducing the cost of healthcare without compromising the quality of service is the main agenda of the regime. The research proposes the smart healthcare monitoring system (SW-SHMS) to address the issues of delivering home-based healthcare monitoring while avoiding hospitalization [24]. This study aims to organize the most recent data on AiP technology that supports older individuals living in communities in their daily activities. These studies examine the effects of various technologies on older people who live in their communities while taking several health-related outcomes into account [27]. The value of employing a social robot was examined in two distinct studies: in therapy sessions for children with autism spectrum disorder, and in older adults’ participation and social interactions. The testing results in both situations were quite positive, demonstrating the advantages of deploying robot platforms as a support tool. Assistance provided to senior citizens is more tailored and suitable, enhancing their independence, well-being, and quality of life [28].

Studies show that it is possible to employ technology in a way that enhances the quality of life of older people. The demand for IoT devices with advanced capabilities to perform more complex activities for elderly people is profound. Recently, the concept of smart homes has garnered significant attention from various sectors, including researchers, industry professionals, and academia. Smart home environments can be controlled through any smart gadget. Wireless communication methods are utilized to monitor and manage household functions through smartphone applications. Cloud computing provides scalable processing power, storage capacity, and applications for developing, managing, and administering home services. It also enables access to home devices from any location at any time. In order to achieve aging in place, a sophisticated modification strategy and a flexible living environment are required to meet the desires of elderly individuals and maximize the effectiveness of smart technology [14]. Cloud computing brings a virtual platform, where all resources, whether software, hardware, or data, can be easily shared among stakeholders with minimal effort.

The integration of IoT and cloud computing will significantly change how people live their lives, particularly in terms of information handling. Depending on the deployment, IoT data can only be analyzed, stored, and accessed through the cloud. Cloud computing offers on-demand information accessibility whenever there is an internet connection on any device. As the adoption rate increases, more businesses are recognizing the benefits of the cloud and the need for using hybrid cloud technology. Cloud computing will continue to open up new opportunities for the IoT [29]. Energy preservation is also an important factor for future generations. The importance of energy conservation will grow as the world’s population grows. With the assistance of these devices, any home can become smart and energy-efficient. This technology can be used to remotely operate a house, offering safety, energy savings, and remote access [30].

3. Proposed HAIE Framework

The existing literature has primarily focused on IoT-based frameworks for addressing the aging-in-place problem. In our proposed model, we not only introduce an IoT-based framework but also integrate it with the cloud platform. The utilization of cloud technology enables remote access and monitoring of data and environments via the internet. The primary advantage of our proposed model is the ability to access data of any size from any location at any time and on any device. Furthermore, we have assessed the performance of our model based on two indicators: accuracy and latency. We considered two case studies and established the justification of our proposed model in relation to the aging-in-place problem.

The proposed Human Activity IoT-enabled Environment (HAIE) framework can improve the relationship between humans, activities, and technology (Reference: Figure 5). IoT-enabled smart home technologies strengthen the connections among all theoretical components, including humans, activities, IoT, and the environment. The integration of technology with AiP improves daily life routines. The patterns of daily life primarily depend on the health condition of individuals, enhancing the interdependencies between activities and technology for the smooth execution of household chores [17]. The research work consists of four stages.

1.

The deployment of sensors in conventional gadgets or the replacement of conventional gadgets with smart gadgets.

2.

The collection of data through deployed sensors or smart gadgets.

3.

The response to accumulated data using IoT-based devices.

4.

The storage of data in the cloud for further analysis

3.1. Sensor Placement in Smart Home

The IoT in a smart assistive home is an emerging topic in many scientific models and novel applications [31]. The smart devices identified in this work are illustrated in Figure 6. The proposed HAIE model showcases several identified devices within an assisted living facility to enhance the QoL for aging in place. The HAIE model introduces the integration of technology with human-centered models, allowing interactions and corresponding actions to be executed through remote sensors. This model aims to enhance the life quality of elderly citizens. Figure 6 illustrates the HAIE model. The home appliances or applications that can be transformed into a smart environment include:

• Smart door locks and automatic sliding doors.
• Motion sensor lights.
• Voice assistance with light and device controls.
• Sensor-based toilet disposal and cleaning.
• Automatic house cleaner and dishwasher.
• Smoke detector.
• Temperature control.
• Automatic sensor-based bed warmer.

3.2. Integrating the Cloud with the IoT-Based HAIE Framework

The resource-constrained architecture of sensors is insufficient for processing and storing data at the local computation unit. To overcome this limitation, integrating the cloud with IoT devices provides access to unlimited resources through a pay-as-you-go model. This integration facilitates the harmonization of heterogeneous devices, protocols, and cross-platform technologies. This is possible by providing limitless processing and storage resources through cloud infrastructures, enabling high computational processes for modern smart home applications. Due to several factors, some appliance and device manufacturers are opting to create exclusive platforms for smart homes that align with their preferences. These platforms often offer their own product and service interfaces, resulting in the implementation of multiple communication standards and protocols within each solution. As a result, the key problems lie in linking diverse devices and services offered by many manufacturers and ensuring smooth interactions across the available platforms [32].

This cloud integration enables coordination with a broad range of intelligent home applications. The public cloud platform, such as AWS IoT, helps address compatibility issues among various smart devices launched by different vendors. The proposed cloud architectural concept for IoT-based smart homes is shown with a layered design in Figure 7. Each layer of the architecture has a specific function, with the bottom layers providing structural support to the top layers. Different IoT devices, including sensors, actuators, controllers, mobile phones, and other home appliances, can be connected by integrating them under a common cloud-based platform using available wireless and wired communication technologies [33].

A software platform known as middleware acts as an intermediary between two applications or devices. It enables the connection between any two clients, servers, databases, or even programs. The middleware layer is responsible for formalizing the collected data and establishing a connection with the cloud. Furthermore, the data are stored in the cloud infrastructure for further processing. The proposed model utilizes a service-oriented architecture (SOA) to connect numerous devices from different manufacturers and integrate various types of information. The development of sophisticated home services can be accomplished by organizing, combining, and packaging smart home apps using SOA. Figure 7 illustrates the layered architecture of the IoT and cloud-integrated HAIE model.

This layered architecture enables the manipulation of IoT devices within the cloud environment. Figure 8 shows the customer and target devices linked to different private platforms. The essential operational steps are explained as follows:

1.

Through the vendor-specific auxiliary application that is loaded on the smartphone, the consumer simply sends a command for action to the appropriate private platform.

2.

Initially, the infrastructure locally validates the device IDs of the targeted devices locally. If the device is not supported by the customer’s connected private platform, the public cloud platform bus receives the operation command.

3.

After addressing the device ID, the platform bus transmits the operation command to the corresponding private platform. The legality of the operation is verified. This private platform is connected to the target device. If the operation command is deemed legal, it is forwarded to the connected intended device.

4.

Once the intended device has carried out the requested modification, it informs its associated private platform of its current status. The public cloud platform bus then receives the relevant information regarding the present operating status of the intended device.

5.

The platform bus integrates the device’s most recent status across the entire cloud platform.

The mapping of device IDs with intended devices helps to identify the details of the query and its source; otherwise, the addressing operation will be carried out by the public cloud platform using the device ID of the intended device, and the operation command will be forwarded to the private platform connected to the intended device. This architecture is particularly useful when the customer and the intended device belong to different private platforms or applications. Implementing this architecture ensures the synchronization of pertinent operating status parameters across the entire cloud platform.

3.3. Entity–Relationship between the Cloud-Enabled IoT Device and User

The ER (entity–relationship) diagram (Figure 9) for IoT-enabled smart devices depicts the various entities and relationships that are involved in an IoT system. Here is an explanation of each entity and its role in the system:

• Device: Represents an IoT-enabled smart device. Each device has a unique identifier (device_id). A device can have multiple sensors and actuators.
• User: Represents a user who interacts with the IoT-enabled device and accesses data via a cloud platform. Each user has a unique identifier (user_id), a name (user_name), and an email address.
• Device_Sensor: Represents a relationship between a device and a sensor. Each Device_Sensor entity has a foreign key reference to the device entity (device_id) and a sensor identifier (sensor_id), a name (sensor_name), and a type (sensor_type).
• Device_Actuator: Represents a relationship between a device and an actuator. Each Device_Actuator entity has a foreign key reference to the device entity (device_id), as well as an actuator identifier (actuator_id), a name (actuator_name), and a type (actuator_type).
• Data: Represents the data generated by sensors and actuators. Each datum entity has a unique identifier (data_id), a value (data_value), and a timestamp (timestamp) from when the data are generated.
• Data_Sensor: Represents a relationship between a sensor and the data it generates. Each Data_Sensor entity has a foreign key reference to the Device_Sensor entity (sensor_id) and a reference to the datum entity (data_id).
• Data_Actuator: Represents a relationship between an actuator and the data it generates. Each Data_Actuator entity has a foreign key reference to the Device_Actuator entity (actuator_id) and a reference to the datum entity (data_id).

3.4. Relationship between the User and Cloud-Enabled IoT Device

1.

The user sends a command to the IoT device, such as turning on a smart light (Figure 10).

2.

The IoT device sends data to the IoT platform, such as the current state of the light (Figure 10).

3.

The IoT platform sends the device data back to the user, so the user can see the current state of the light (Figure 10).

4.

The user sends a command to the IoT platform, such as setting a schedule for the smart light (Figure 11).

5.

The IoT platform sends the command to the IoT device, which sets the schedule (Figure 11).

6.

The IoT device sends data to the IoT platform, such as the updated schedule (Figure 11).

7.

The IoT platform sends the device data back to the user, so the user can confirm that the schedule was set correctly (Figure 11).

8.

The user requests the current status of the IoT device, such as the current temperature of a smart thermostat (Figure 12).

9.

The IoT platform requests the device status from the IoT device (Figure 12).

10.

The IoT device sends the device status to the IoT platform (Figure 12).

11.

The IoT platform sends the device status back to the user, so the user can see the current temperature (Figure 12).

Each of these scenarios involves a series of interactions between the user, the IoT device, and the IoT platform, as represented in the sequence diagram. The diagram shows the sequence of events and the flow of data between the entities, allowing developers to better understand the system’s overall architecture and behavior.

4. Framework Deployment

An application framework has been employed to evaluate and compare the integration of IoT in case studies, visually showing how IoT facilitates the perception of surroundings and AiP for older individuals. The HAIE coordination serves as the foundation for linking health, care activity, IoT-based smart home technology, and the surrounding environment in constructing the framework. This framework serves as a roadmap for analyzing the relationship between IoT, the cloud, and AiP. The case study framework developed for this study illustrates the impact of introducing new technology on the care needs, well-being, and independence of elderly individuals at home. It also provides an opportunity to record some of the difficulties and constraints experienced by the elderly person, their family members, and caregivers during the implementation and use of the technology. The framework divides the case study into seven categories: personal profile, functional limitations, daily activity, IoT-friendly environment, IoT-enabled technology, outcomes, Each section is assigned a numerical designation, as shown in Figure 13.

• Depiction of the HAIE framework model:

The first five (1, 2, 3, 4, and 5) components of the proposed framework depict the components of the HAIE model, and components 6 and 7 summarize the findings of the case study. Figure 14 illustrates how the framework maps each HAIE component.

1. The personal profile features the formal and informal care required and depicts the older person’s situational, health, and functional profile.

2. The functional limitations module provides an example of how an older person’s health condition can impact their ability to live independently at home.

3. The daily activity module illustrates some of the daily activities that an older person engages in.

4. The IoT-friendly environment records any house adjustments being made as well as obstacles that people encounter that may inhibit them from performing chores independently. It includes precise details regarding the settings that enable IoT-friendly caregiving.

5. The IoT-enabled device section presents the IoT device and outlines its intended purposes.

6. The outcomes section summarizes the findings from all sections of the framework and details how technology affects caregiving, senior health, well-being, as well as caregivers. It examines the benefits and limitations of the device used for caregiving.

7. Case study highlights provide an overview of the broader issues addressed within the case study.

5. Case Studies

Two real case studies were selected for examination and put into the framework to comprehend how IoT and cloud integration can affect caregiving in the home. This will show the impact of IoT and evaluate the framework’s effectiveness in supporting future research and validating the HAIE hypothesis. Each older person included in the study received care at home. The technologies encompass diverse fields, including a wearable smartwatch and a smart voice assistant. Both technologies support the cloud. The case studies investigate the interaction between specific IoT applications and different caregiving approaches within the home environment to facilitate AiP. The case studies are presented using the aforementioned framework. They include a diagram illustrating how these components work together to allow senior citizens to handle challenging chores safely and independently at home.

5.1. Case Study 1: Vijay: IoT-Wearable Device: Smart Watch

Vijay, an 85-year-old cardiac patient, is becoming more fragile, and has recently undergone coronary artery bypass heart surgery. He is a widower, and his family lives interstate. Vijay prefers to live alone at his home in a small town (Figure 15). Jyoti, Vijay’s daughter, is concerned about her father’s health, so she gifted him a smartwatch (an IoT-based wearable) to keep track of Vijay without disturbing his independence. The deployment of case study 1 can be viewed in Appendix A Figure A1b.

• Vijay’s Outcome:

Vijay’s experience with keeping track of his health has been positive. His daughter, Jyoti, feels relieved and believes that Vijay is safe while living independently. She is able to monitor all of Vijay’s health activities and consult with the doctor using the reports provided by the watch. The watch offers various healthcare features [34]:

• Heart rate notifications;
• Irregular rhythm notifications;
• ECG application;
• Low cardio fitness notifications;
• Blood oxygen level;
• Fall detection.

• Highlights for additional research in pattern recognition:

Vijay’s case study emphasizes the importance of technology in promoting well-being and how preserving autonomy can enable the continuation of traditional family roles despite changing medical requirements. The technology, which can be remotely controlled over the internet through an app and the cloud, was not extensively utilized or valued by Vijay. Due to Vijay’s declining health, he became unable to perform a crucial activity in his life. Therefore, Jyoti finds this function to be very beneficial as it allows her to access his vitals via the app. Here are some of the key highlights from the case study:

• In response to a particular task, smart technology may be used in conjunction with home alterations.
• We investigate how new technologies are purchased and maintained by elderly individuals on restricted budgets.
• We consider how smart technology can promote independence and self-care, improving wellness by boosting feelings of value and independence.

• Limitations of Smart wearable

Even though the watch has many features that help Jyoti keep track of Vijay’s health, it also has some disadvantages:

• The watch is expensive; thus, many people cannot afford it.
• The watch has a maximum battery backup of 20 h, which means it must be charged almost daily.
• The watch requires active cellular or Wi-Fi connectivity to transfer data.
• The watch requires high maintenance.

5.2. Case Study 2: Uma: IoT-Based SVA (Smart Voice Assistance)

Uma, a 70-year-old Indian woman, visits the neurology department upon the recommendation of her primary care provider due to concerns about mood swings and memory problems. The patient states that she has been forgetting things. The prior day, she could not remember her cat’s name. She stood there staring at her cat for an extended period of time, unable to remember. It was a dire situation for her. The patient also reports difficulties in remembering phrases needed to express herself at times. She is worried about her forgetfulness as well. She is constantly misplacing things and neglecting what she is doing. The patient is not overly concerned about these symptoms; she states that, usually, she is fine. Uma’s husband, Rajiv, reports that she often repeats herself and forgets important details; he notes that the symptoms appear to be worsening. For the past few years, Uma has noticed some nebulous symptoms, and during the last four to five months, these symptoms have become more regular. Uma and Rajiv both agree that she has not experienced any difficulties carrying out her daily work or other routine activities. Uma mentions that her mood swings started two months ago. When pressed for more information, she becomes easily annoyed (Reference: (Figure 16)). The deployment of case study 2 can be viewed in Appendix A Figure A1a.

Uma’s outcome: The SVA device turned out to be very beneficial for Uma, as all of her problems related to memory loss have improved, along with her confidence, as stated by Rajiv, who feels safer and less worried about Uma. This is possible only because of the new technology included in our SVA device, which offers features such as remote screening and security control. This has led Uma to live a better and more comfortable life, even without Rajiv.

• Features of SVA

• Reduces memory loss.
• No more misplaced items.
• Improves problem-solving.
• Increases social initiatives.
• No confusion regarding time and location.

• Highlights for additional research in pattern recognition

Uma’s case study highlights various issues, including the introduction of technology, the management of external influences, and the integration of technology with modifications to the physical environment, which may seem unrelated. One significant aspect of the broader discussion is the role of family members in initiating and implementing technology in the homes of older individuals. Another highlight is the impact of power outages on smart home technologies and the ability of elderly individuals to resolve ensuing issues. House modifications, combined with smart technology tailored to specific needs, are crucial. In this context, smart voice assistance (SVA) is a highly valued attribute of this technology. It allows for remote screening and the control of security, ensuring that Uma’s well-being is maintained. Additionally, Uma can provide decision-making support without the need for Rajiv to be physically present.

• Limitations of SVA

SVA has helped Uma in many aspects, including remembering her day-to-day tasks and maintaining her social life. Although SVA has many benefits, it still has a few drawbacks:

• The initial cost could outweigh savings.
• Security.
• Voice recognition is not perfect.
• Dependency on the internet.

Figure 15 and Figure 16 use the HAIE-based map to illustrate the data from the two case studies, recognizing the clear connection between the IoT-enabled environment, formal and informal care, and contemporary technology for AiP. In light of this, the suggested multi-layer cloud architectural approach, as illustrated in Figure 8, using the public cloud layer stated above, facilitates the smooth integration of auxiliary applications (often private clouds) offered by diverse manufacturers. This enables efficient, on-demand, low-cost, real-time home services. In the first case study, Vijay lives in a small village with outdated technology. He is less supported as he lives alone. In case study 2, Uma and her husband reside in a city. She has the right assistance.

Even if the high-tech gadgets stated above are quite practical and dependable, occasionally, an internal bug, network issue, or server malfunction might render these devices worthless. In Uma’s example, if the voice assistant freezes or stops working as a result of a network outage or server malfunction, all of her chores and tasks are disrupted. This makes life challenging for Uma and her husband. In Vijay’s situation, all of his health readings disappear, and Jyoti is unable to access them via the companion app, making it incredibly challenging for her to monitor her father’s health status from a different location or city. The two case studies illustrate the connection between a person’s health (human), capabilities (activities), smart technologies (IoT), and the IoT sustainable home (environment) through a small sample of numerous real-world examples used in the homes of elderly people.

6. Results and Discussion

The proposed HAIE model has been validated by considering two factors: latency and accuracy. Latency involves the timely transmission of data so that the appropriate actions can be executed. In the proposed model, the data are transmitted from smart gadgets to the local server at home and then to the cloud environment. Thus, the total latency ($T_l$) can be calculated by using Equation (1).

$$T_l=N_l+C_l$$

(1)

where $N_l$ represents latency in the network and $C_l$ represents latency with the cloud. The total latency of the HAIE model-based case studies can be calculated through the following equations. The network delay $N_l$ can be calculated by using Equation (2), where $P_d$ is processing delay, $Q_d$ is queuing delay, $T_d$ is transmission delay, and $P{R}_d$ is propagation delay. $C_l$ represents the delay, which is the time difference between sending a data packet from the client’s side and receiving a response from the server end. For the proposed work, we considered $P_d$ and $Q_d$ as 0. $T_d$ is calculated by dividing the number of bits by the transmission rate. Moreover, $P{R}_d$ is calculated by dividing the distance by the speed of the packets.

$$N_l=T_d+P{R}_d+P_d+Q_d$$

(2)

For the given case studies, we have considered a transmission rate of 1 Mbps and a propagation delay of 54 ms. Hence, the time required to transmit 1 kbps of data can be calculated by using Equation (2).

$$N_l=\frac{1000}{1000000}+\frac{80}{1000}+0+0=0.054\mathrm{s}=54\mathrm{ms}$$

(3)

The total end-to-end latency ($n_{lt}$) for the cloud can be calculated by adding the bootstrap time ($n_{bt}$), connection time ($n_{ct}$), and transmission time ($n_{tt}$). $n_{tt}$ is the combination of three other components, i.e., the sending time $n_{st}$, uploading time $n_{ut}$, and response time $n_{rt}$, as defined in Equation (4). We assume an idle latency of 30.35 ms over the cloud as it uses 4G networking with no congestion environment to communicate with the cloud storage.

$$n_{tt}=n_{st}+n_{ut}+n_{rt}$$

(4)

Hence, the total latency ($N_l$), i.e., from smart gadgets to the cloud environment, can be calculated by using Equation (1).

$$N_l=54\mathrm{ms}+30.35\mathrm{ms}=84.35\mathrm{ms}$$

(5)

In Figure 17, latency plots are presented for IoT devices with the required rates of 200 Kbps, 500 Kbps, and 1 Mbps. The results show that for a required rate of 200 Kbps, increasing the number of IoT devices results in higher latency compared to 500 Kbps and 1 Mbps. This finding confirms the notion that latency increases as the required data rate increases. As a result, reducing end-to-end latency can lead to lower energy consumption at every step, as IoT devices can be served more quickly. In the proposed study, the consideration of only two devices resulted in lower latency.

Another factor is accuracy; under this, we collected the alarms that were generated by the proposed model. Here, from the set of observed alarms ($n_o$), the number of accurate alarms ($n_a$) and false alarms ($n_f$) can be calculated by using Equation (6).

$$n_o\%=\frac{n_a-n_o}{n_a\times 100}$$

(6)

The accuracy of smart devices can be particularly important for older individuals who may have more difficulty with manual tasks or may require additional assistance with daily activities. For example, an automatic sliding door that fails to properly acknowledge a person’s presence could become a safety hazard for an elderly individual with limited mobility. On the other hand, a voice assistant with a high accuracy rate, combined with a light feature, can provide a convenient and reliable way for elderly individuals to control their home environments without the need to manually operate switches or knobs. Similarly, a sensor-based bed warmer with a high accuracy rate can provide a comfortable and consistent sleeping environment for an elderly individual, which may be particularly important for those with mobility issues or chronic pain. By carefully considering the accuracies and performances of smart devices, caregivers or family members can select devices that are safe, reliable, and convenient for older individuals, ultimately helping to improve their quality of life. Figure 18 illustrates the accuracy rate by using Equation (6).

Critical Analysis

The analysis and assessment of various models referenced in the literature study demonstrate the efficacy of the HAIE model. The distinctions between the HAIE model and the other models referenced in the critical analysis are seen in

Table 1. Critical analysis of the existing and proposed models.

Models	Eliminates			Met Expectations			Formal/Informal Care Required			Cost	Cloud Enabled
	Care			People			Timely
	Formal	Informal	Self	Older Person	Family	Care Taker	Hourly	Daily	Weekly
HAS [35]			✔	✔			✔			High
IoT-CHO [20]			✔	✔				✔		High
IoT-H2U [19]		✔	✔	✔				✔		High
HS-IoT [36]			✔	✔		✔				Low
ICE [37]		✔	✔	✔	✔		✔			Moderate
HAST [17]	✔		✔	✔	✔			✔		Moderate
NOAH [22]		✔	✔	✔	✔	✔	✔			Low
Proposed HAIE	✔	✔	✔	✔	✔	✔		✔	✔	Moderate	✔

. A total of five components are illustrated in Table 1.

• Eliminates or minimizes: formal care, informal care, self-care.
• Meets expectations: older person, family, formal/informal care.
• Time of formal/informal care required: hourly, daily, weekly.
• Cost: low, moderate, costly.
• Cloud-enabled.

The names of many models that were referenced in the literature review are listed in the “Model” column in Table 1. The “Eliminates or minimizes” column in Table 1 shows the decline of formal, informal, and self-care requirements. The “Met expectations” column in Table 1 demonstrates the level of acceptance and belief in the model by the older person, their family, and caregivers. The “Formal/Informal Care Required” column illustrates when (and how much) formal/informal care is required for the older person who relies on the model. The “Cost” column in Table 1 illustrates how reasonably priced the model is.

• Eliminates or minimizes: formal care, informal care, self-care
• Meets expectations: older person, family, formal/informal care.
• Formal/informal care required: hourly, daily, weekly.
• Cost: low, moderate, costly.
• Cloud-enabled.

The model proposed by HAS (2005) satisfies self-care and meets the expectations of older persons, but it requires hourly-based care at a high cost. The IoT-CHO (2015) model is also engaging, targeting older people who require daily care at a high cost. IoT-H2U (2016) adds self-care as an option to informal care, meeting the demands of elderly persons for high-quality care but at a high price. The proposed model HS-IoT (2017) satisfies self-care for older persons with formal/informal care at a low cost. In 2018, the ICE model with the same functionality as IoT-H2U but with a moderate cost was proposed. ICE (2018) focuses on meeting the expectations of the family. Along with ICE, the HAST (2018) model addresses the requirements of formal care, self-care, older persons, family, and daily care, at a reasonable price.

The HAIE model reduces the older person’s requirement for formal, informal, and self-care. For example, Vijay’s smartwatch reduces the need for formal care by regularly monitoring his vitals and sending reports to his doctor and daughter. Another example is Uma’s smart voice assistant, which assists her in remembering daily tasks and reduces the need for informal care. The HAIE model effectively meets the needs of older individuals, their families, and caregivers. Uma’s voice assistant has met her expectations by helping her remember things and live a more independent life. Jyoti, Vijay’s daughter, also had her expectations met as she received regular updates on her father’s health through the smartwatch. Generally, an older person using the HAIE model requires formal care once a week and informal care once a day. For instance, Vijay’s smartwatch requires daily informal care for charging, and his vitals are sent to the doctor weekly for routine check-ups, requiring formal care once a week. The HAIE model incorporates devices with a range of affordable to moderately priced options. For instance, smartwatches are available at various price points in the market, ranging from INR 2000 to INR 6000.

7. Limitations and Future Work

In the technology field, new advancements can occur so quickly that anticipating what might happen in the future may be pointless, as these advancements often occur as soon as they are predicted. While the future of smart homes is promising, we must adopt a comprehensive perspective and recognize the drawbacks that such expansion brings.

• Security concerns: With the increasing use of smart home gadgets, security concerns are likely to become a major issue, similar to other computing equipment. Consequently, many ecurity issues are expected to emerge, leading to a surge in the demand for smart home security software and equipment.
• App security: Smart home gadgets typically come with companion apps that enable users to operate them. However, these apps require various permissions that affect the device’s functionality, such as the ability to lock and unlock smart home security systems. If hackers manage to gain access to these apps, they could potentially gain entry to one’s home, leading to serious security implications. To mitigate these risks, it is crucial to keep smart home apps up-to-date and apply software and security upgrades.
• Wireless security: Wireless connectivity (Wi-Fi or Bluetooth) is essential to the functionality of almost all smart devices. However, as with all digital communications, wireless connections are susceptible to interception by hackers, who can exploit information to gain unauthorized access to smart home equipment. Therefore, it is crucial for people to secure their home Wi-Fi routers since Wi-Fi is one of the most commonly used methods for connecting to smart home appliances.
• Cost: While many smart home gadgets are now affordable for most people, fully outfitting a home with such devices can still be costly.
• Greater acceptance: As with many new technologies, some individuals may perceive smart home technology as unnecessary. However, similar to the evolution of essential home appliances, such as washing machines, microwaves, and TV remote controls, it is probable that in a few years, the notion of controlling one’s lights with voice commands or utilizing a robotic vacuum cleaner to clean one’s house will be widely accepted and commonplace.

Furthermore, this work can be extended through the development and implementation of artificial intelligence (AI), machine learning (ML), deep learning, and futuristic technologies such as robotics and 3D printing, which have significantly contributed to advancements in the health sector. Since deep learning is a subset of machine learning, deep learning is capable of achieving larger-scale performance. It utilizes more data and has a higher level of autonomy. AI and deep learning-based technologies are currently influencing numerous aspects of consumer life. The efficiency of HAIE can increase with the help of AI and deep learning. Home objects can be linked to systems that utilize deep learning to identify trends in human behavior, and to forecast things, such as the need for heating and lighting. Integrating machine learning and deep learning can introduce an automated learning pattern in the proposed model. This will help to address the new problem of aging in place.

8. Conclusions

This paper presents our HAIE model and compares it with previously developed IoT-based models for aging in place. The primary objective of the HAIE model is to understand the relationship between a person’s health (human), capabilities (activity), smart technologies (IoT), and the IoT sustainable home (environment). The HAIE model also focuses on how IoT-enabled devices in the built environment help AiP. In this paper, two case studies are illustrated that show how technology interacts with elderly people. The specific relationships between a person’s functional capacity and built environment are outlined in the HAIE model. Technology, in conjunction with IoT, will transform the future of elderly care. This model has the potential to improve illness trajectories and AiP. There is a possibility that older people may lack confidence in technology, become easily frustrated by it, struggle to maintain the devices, or be unable to afford the technology. Elderly people can benefit from this model and do not have to worry about leaving their homes due to their age. This model helps to maintain a social balance through technology. Additionally, this model satisfies various critical sections, such as formal care, informal care, and self-care. The integration of continuous monitoring services into a home automation platform from a remote facility has been made possible through significant advancements in technology, enabling the development of low-power, small, and cost-effective sensors and actuators, combined with contemporary communication technologies. The advancement of technology has resulted in the integration of IoT-enabled gadgets into popular electronics ideologies, and more individuals are connecting smart assistive devices into their lifestyles.

The threshold amount of energy is a primary concern for ensuring the smooth operation of smart devices. To enhance the system’s running time, advanced battery technologies and low-power electronic components can be used to maximize the operational times for IoT assistive devices. In conclusion, smart home technologies are a valuable area of research to support aging in place. IoT is well-suited for integration with cloud architecture. IoT may profit from the limitless resources and capabilities of cloud computing due to the scalable powers of the cloud. Using cloud computing and IoT, data can be stored in the cloud for future reference, in-depth analysis, and enhanced performance.