Towards Privacy-Preserved Aging in Place: A Systematic Review

Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required.


Introduction
Progressive population aging is a global phenomenon. Improvements in public health, medicine, nutrition, and workplace safety standards have contributed to higher life expectancy. According to a United Nations report, the population aged ≥65 years is projected to be approximately 2 billion by 2050 [1]. This exponential increase in the aging population is liable to impose a significant burden on the socioeconomic well-being of many countries. Healthcare systems across the world will face the challenge of delivering efficient services to better educated, elderly population within strict budgetary constraints.
Elderly individuals tend to face difficulties in carrying out routine daily activities [2], which may make them dependent on caregivers or family members. In addition to the increased prevalence of comorbid conditions, elderly individuals tend to develop cognitive impairment with progression of age. Low physical strength [3], age-related dementia [4], depression [5], behavioral changes [6], and compromised communication skills [7] are some of the other issues that contribute to the increased dependency of elderly people.
Placement of the elderly in a nursing home or a care facility against their will has a detrimental effect on their well-being; it often leads to social isolation, depression, and greater dependency for completing self-care tasks [8]. Elderly people typically prefer to (IoT) has increased, creating a network of different objects, like sensors, actuators, mobile devices, tablets, etc. [28].
These remote monitoring technologies use sensors [29][30][31][32] and cameras [33] to monitor the elderly, detect falls or for emergency situations. Developments in technology have led to usage of wireless sensor networks placed all over the house at designated points, which collects the data and is usually processed and analyzed by controlled thru a suitable algorithm to give report to the caretaker or healthcare provider. Various types of sensors, have been used, be it environmental sensors, water sensors, temperature sensors, wearable sensors to keep track to numerous activities like, water usage, sleeping patterns, walking patterns, eating patterns, etc.
Several reviews have assessed the requirements of older adults in the context of home-based health care or telecare [34,35]; however, two systematic reviews specifically examined the facets of aging in place, i.e., a systematic review on cost effectiveness of aging in place [36] and a systematic review of acceptance of technology for aging in place [37]. The authors of the first review [36] found that the existing technologies were of low quality; in addition, the authors were not able to draw any definitive conclusions owing to lack of standardization of measurement indices in various studies. The technology acceptance review identified issues pertaining to discretion and affordability, control and freedom, and anxiety of stigmatization and institutionalization [37]. These two reviews have contributed significantly to the understanding of the concept of aging-in-place; however, each review was focused on a single aspect of the lived experience. Another review [38] synthesized and evaluated the existing qualitative evidence pertaining to aging in place in the US. The value of aging in place is determined and affected by various factors such as culture and differences in economic and social structure [39]. This review [38] was based on experiences in the US and the findings may not be entirely generalizable to other countries.
Few reviews talk about the application of AI technology to smart homes. One review [40] evaluated the intelligent surveillance systems in smart home environments, another review [41] investigated philosophical keystones and how they assist healthcare workers, scientist to collaborate with engineers to develop intelligent health-assistive smart homes. Kumar et al. [42] discussed the different variety of home automation systems and how they use AI tools. These systems were mostly applied as comfort ability, remote control, optimal resource utilization, and security.
It was noted that there were very few reviews which target the protection of privacy in the smart homes designed for the elderly. Given the availability of literature there is a need to review the current strategies and how they can be further enhanced. The current status of the research needs to be analyzed and evaluated for the various features that constitute in development of smart home, how each of these features can be further advanced in terms of technology and usability, how they can be made secure and unobtrusive, so that the users do not feel that they are under surveillance. The objective of this systematic literature review was to identify the different types of smart-home solutions or technology-based strategies available to assist the elderly to live independently in their homes and assess the current state of privacy preserving technologies incorporated into these homes.

Methods
A systematic literature review (SLR) condenses existing proof, identifying holes and identifies directions for future research.

Search Strategy
An extensive literature research was performed in the PubMed, SCOPUS and IEEE Xplorer databases. The keywords used were "smart homes", "elderly", "aging in place", "non-intrusive", "privacy preserving", and "independent living". They were used in combinations like the following: • Smart Home AND (Elderly OR Aging in place) OR (Non-Intrusive OR Privacy) • Smart Home AND (Elderly OR Aging in place) AND Non-Intrusive

• Smart Home AND Elderly AND Privacy
Using advanced search techniques, the databases were searched, in combinations of these terms appearing in all metadata (title, abstract, keywords, full text) and were later screen according to various criteria mentioned in the next section.
The retrieved articles were shortlisted, and duplicate publications were removed. Subsequently, the titles and abstracts of the remaining studies were screened against the following criteria.

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The inclusion criteria were: • Studies published in English.

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Studies that used technology in the home, both technologies embedded in the home or independent technology (such as a robot).

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Addressed the needs of older adults living independently both healthy and elderly with health issues (monitoring of activities of daily living or health). • Studies that entailed implementation or deployment of technology, even if in a pilot form, or proposed studies, to assess the feasibility and outcomes.

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Studies that were published within the last decade, so that the latest researched were included.
The exclusion criteria were: • Studies published as academic theses. • Studies which were reviews, book chapters. • Studies which were not health-related and focused on other aspects such as energyconservation or security surveillance systems

Results
A total of 1319 studies were identified after title and abstract screening, out of which 65 were accessed for full text reading and as a result 31 were finally included in the review. The features available for smart home systems that were found in the shortlisted studies can broadly categorized into the following categories:

Application of Environmental Sensors, Wearables and Cameras
Twenty-nine studies  included in this review entailed the use of various types of sensors; mostly environmental sensors, as the key elements involved in the functioning of a smart home. These studies entailed deployment of entire sensor-based network systems or placement of various sensors all over the home for monitoring the ADL and the overall well-being of subjects.

Results
A total of 1319 studies were identified after title and abstract screening, out of which 65 were accessed for full text reading and as a result 31 were finally included in the review. The features available for smart home systems that were found in the shortlisted studies can broadly categorized into the following categories:

Application of Environmental Sensors, Wearables and Cameras
Twenty-nine studies  included in this review entailed the use of various types of sensors; mostly environmental sensors, as the key elements involved in the functioning of a smart home. These studies entailed deployment of entire sensor-based network systems or placement of various sensors all over the home for monitoring the ADL and the overall well-being of subjects.

AI Machine Learning and Robots in Smart Homes
Twenty-seven 65,67,72,73] out of the 31 studies used AI, machine learning, or robots in their smart home techniques.

User Feedback, Satisfaction and Effects of Smart Homes
Eight [43,44,58,60,63,66] of the 31 studies included in this review, the systems deployed were aimed at providing some sort of medical support by health monitoring and taking appropriate action. Fourteen [43,45,[47][48][49][51][52][53]55,60,64,65,69,72] of the 31 studies were aimed at monitoring the environment for any abnormalities and detecting falls, which allowed the elderly to stay alone in their homes. Figure 2 shows the numbers of studies with different features of smart home. This figure enlists in detail features like wearable, body sensors, environmental sensors, cameras, voice command feature, Al or ML capabilities, robots, privacy preservation, fall detection and monitoring of daily activities. Each bar represents the number of studies out of 31 which have included the mentioned feature. taking appropriate action. Fourteen [43,45,[47][48][49][51][52][53]55,60,64,65,69,72] of the 31 studies were aimed at monitoring the environment for any abnormalities and detecting falls, which allowed the elderly to stay alone in their homes. Figure 2 shows the numbers of studies with different features of smart home. This figure enlists in detail features like wearable, body sensors, environmental sensors, cameras, voice command feature, Al or ML capabilities, robots, privacy preservation, fall detection and monitoring of daily activities. Each bar represents the number of studies out of 31 which have included the mentioned feature.  Table 1 below summarizes the key characteristics of these studies. Table 2 enlists the different features and tools used in the shortlisted studies, such as wearable devices, environmental sensors, cameras, robots, voice commands; in addition, we assessed whether these systems had safeguards to protect user privacy.  Table 1 below summarizes the key characteristics of these studies. Table 2 enlists the different features and tools used in the shortlisted studies, such as wearable devices, environmental sensors, cameras, robots, voice commands; in addition, we assessed whether these systems had safeguards to protect user privacy. • Collection and labeling of sensor data was done using activity recognition and BCD was applied to analyze behavioral changes. • Analysis of changes in the timing and duration of activities was also done.
• Evaluation indicated that the behavioral changes observed in these cases were in line with the medical literature and that the variations can be automatically spotted using Behavior Change Detection (BCD). • Sensor-driven recognition of activity errors and the need for assistance.

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Searches and locates the resident, provides video reminders, and guides them to the needed objects for the missed activity steps.
• Physical assistance is provided by showing residents the location of key objects in the home that are required for daily deeds.
• Satisfactory impressions of the RAS tablet interface. • Neutral and highly variable rating of system usability.

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Questionnaire ratings were not related to age or comfort with system.

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The full script video was found to be puzzling and not very helpful to someone with MCI as compared to the "next step" video and object guidance. The AR activity recognition algorithm allocated a particular activity to each sensor entry.

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The system computed the daily sleep and movement patterns, 17 behavior patterns, time utilized in some specific ADLs, and the overall characteristics of the daily routine. • CAAB algorithm was applied to the data to obtain the behavioral figures of each assessment period.
• Activity-aware smart home data can foretell all mobility, depression, and cognition/memory symptoms as well as a consistent variation in movement and visuospatial skills related to cognition. • Equal contribution was done by behavioral features in the prediction of every symptom. • By way of data from activity-aware smart home, as well as a consistent change in these scores, prediction of Total IADL-C score and sub scores can be done • Detection of positive and negative fluxes in everyday functioning is difficult using in-home behavioral data; however, alterations in social skills were predictable • A fall detection system comprised of a tri-axial accelerometer and gyroscope. • Heartbeat sensor, pressure sensor and temperature sensor to monitor the health and wellness. • 27SMS alert to the caregiver in case of any anomaly

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The system is efficient and consumes less power and provide a safe and secure living to the elderly.

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The STRETCH platform is tri-layered system: a sensor-based network; centralized data analysis layer; and intervention layer.

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The collected data from the sensors is communicated to the central server securely through internet by the gateway. The communicated data is encrypted and is secured via a password.
• STRETCH enables the data integration from sensors and their real-time transmission; it allows the sharing of ADL information. • Smooth monitoring of medicine consumption behaviors was possible with the use of sensors, while community social prompting was done by the mobile social networks.
• Use of the system improved medication adherence by the subjects. • Three activities of daily living were monitored: urination, kitchen work, and maintenance of physical hygiene. These activities are essential for a healthy lifestyle and are associated with tap water usage. • Utilized water-flow sensors, infra-red-based motion sensors, and radio-frequency identification (RFID) receivers to screen the everyday life activity of an elderly and to identify any irregular situation.

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The prototype was tested in a real home and it yielded anticipated results from the water flow based sensors. 22 Suryadevara et al. 2013 [64] Healthy elderly Sample Size not mentioned • The ability to establish the well-being of an elderly living alone in a smart home using an economical, robust, flexible, and data-driven intelligent system. • Non-visual sensor data and pattern recognition technique was powered by this data, was utilized based on low-cost, unobtrusive sensors. Distinctive behavioral patterns were learnt without compromising the privacy of the user. It warned caregiver(s) in case of detection of any abnormality.
• A typical household can be easily converted to a smart household using this system. • Speech-based interaction between 10 elderly human subjects and robots was studied to identify the issues or confusion that can pen.

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The interaction during an ADL is challenging to detect and highly prone to a "lack of uptake," which is the most common problem indicating verbal behavior among subjects The interaction with the robot was done on a daily basis; the tasks included reminders, fetching objects, and entertaining. Recognizing speech automatically, text-to-speech conversion, recognition of gestures, and a graphical user interface based on touch.
• The core task was executed very accurately by the robotic system. All tasks were performed by the elderly together with the robot and was evaluated as usable and acceptable.

Discussion
This systematic review was conducted to showcase the range of currently available smart-home technologies that improve the quality of life of the elderly, while maintaining their privacy and comfort. Thirty-one studies conducted during the period 2010-2020 were included in the review. The topic of smart homes is very broad and can be looked at through many different perspectives: security [74], safety [75], health monitoring [76], social interaction [77], general well-being [43], support for carrying out activities of daily living, timely reminders for certain tasks or intake of medications. This review mainly focused on the aspects of health monitoring and environmental monitoring with use of technology involving the use of sensors, wearables, and robots. In addition, we assessed any potential concerns pertaining to the privacy of users.

Application of Environmental Sensors, Wearables, and Cameras
The smart-home technologies are now becoming increasingly non-intrusive as more people are becoming aware of the dangers of privacy breach; at the same time, these entail continuous monitoring of the well-being of the user. Most of the technologies are for monitoring the health of elderly people through use of sensor-based platforms, wearable devices, robots, or by simply monitoring the environment to notice any unusual activity or anomaly. These systems generate reports or inform the caregiver in case of any anomaly.
Kim et al. [43] used sensors to monitor the mental health of the subject; these sensors helped detect normal behavior or mild depression with 90% accuracy. Deen [44] used sensing technology to monitor the walking patterns, which helped in early detection of muscle weakness, fall, and improper sleeping patterns. Lotfi et al. [55] employed wireless sensors and a computerized base station to monitor patients with dementia who were living independently. The information from the sensors was transmitted to care givers through a centralized portal and helped predict any anomalies. Do et al. [58] used a system which included home-based robot, sensor network, body-based sensor network, a mobile device, cloud-based servers, and remotely accessible caregivers. Grguric et al. [65] developed a sensor-based system with AI capability to learn the subject's behavior over time and detect any abnormal behavior. Yu et al. [66] medication adherence in the subjects. Tsukiyama [63] used water-flow sensors, IR-based motion sensors and radio-frequency identification (RFID) receivers to screen the daily activities of an elderly and sense any atypical conditions. The water flow sensors monitored the usage of water during urination, kitchen activities, and self-hygiene to maintain a check on any deviation from a healthy lifestyle.
Environments and platforms using several types of sensors (such as motion sensors, water sensors, light sensors placed at designated spots in various parts of the home) help in carrying out ADL, fall detection, and other activities. The usage of camera and wearables was very limited. In the 31 studies reviewed, only five studies [46,[66][67][68]71] entailed the use of a camera. Wilson et al. [46] used to monitor the elderly with their permission. Portet et al. [67] used camera only for social communication between the elderly and their families and friends. Yu et al. [66] used a fall detection system based on computer vision, while. Hattink et al. [68] used camera as a part of their surveillance system to monitor falls and emergencies. Hu et al. [71] used a camera array to detect falls and alert the caregivers.
Wearables do not pose any threat to privacy, however, it is not feasible or practical to constantly wear one. Moreover, wearables typically have a relatively short battery life, require maintenance, and cause discomfort over long usage if required to wear consistently throughout the day., may lead to allergic reactions, rashes [78]. Six studies [44,50,58,59,61,71] out of 31 used wearables in the form of body sensors, smartwatch, and monitors. Kshirsagar et al. [50] proposed a wearable glove-based system, with embedded flexural sensors, microcontrollers and Bluetooth features. The gesture generated signals which controlled various home appliances through a mobile application. Jekel et al. [52] included subjects with MCI along with healthy older adults, they carried out their study by setting up a two-room furnished flat with sensors on the items in the house and the subjects were asked to carry out simple tasks, sensor data was monitored and helped in the prediction of MCI or deteriorating cognitive functions. Hattink et al. [68] developed a system called Rosetta for their MCI and dementia diagnosed subjects. The system consisted of three subsystems, one supported in carrying out all the daily activities, another system recorded the data for analysis, third system detected any anomalies, emergency situation like fall or medical assistance. However, certain challenges have not been addressed. These include use of sensors in the washroom or during bathing, sensors to detect falls, and sensors which can distinguish between the elderly and their pets (animal companions to cater to the loneliness) or visitors and residents.
Rizvi et al. [54] developed an Android-based system, comprising of two modules-a GSM module and a Bluetooth module-which allowed users to control the home devices both remotely and locally through custom designed mobile application. The targeted subjects were elderly and handicapped people. Nisar et al. [56] also developed an android based smart home system where the application had three modules: the sensor module, the controller module and actuator module. Sensor-based devices could be accessed through smartphones both remotely and locally, thus making life efficient for the elderly while also reducing power consumption.

Security and Privacy of Data
Security and privacy are key concerns when designing a smart home. Users are generally apprehensive about their privacy as well as data security [79]. Proper ethical agreement must be obtained prior to the use of any video or IP cameras for observation purpose to allay these concerns. Since the end devices frequently transmit data to a central controller, simple eavesdropping attacks can lead to data leaks; the types of end devices can expose the identity of the user. Thus, potential attackers can infer when the house is vacant or identify who is present in the house so that they can break in or cause severe situations. However, none of the 31 studies had explicitly mentioned any such data security feature in their technologies.
A trend was discernible in the 31 studies, wherein studies carried out in recent years accorded due attention to make the technology as non-intrusive and privacy-preserving as possible. Security and privacy are major areas of concern as duly pointed out in several studies [80][81][82]. They have used non-intrusive techniques for achieving their target outcome with the help of different kinds of sensors, wearables and robots. We have developed several deep learning models [83][84][85][86][87] based on privacy-preserved activity and posture recognition tasks; however, three [85][86][87] of these studies were for eldercare ADL monitoring, these studies are not included in this review paper as they have employed an open dataset. Also, most of these techniques do not employ cameras in order to make the users more comfortable. Less usage of cameras and wearables is an added advantage.

AI Machine Learning and Robots In Smart Homes
AI and machine learning are two remarkable innovations that can help in the development of highly advanced and smart strategies. Utilization of AI, machine learning, and fuzzy logic can render the systems more efficient and help them produce more reliable and accurate results. Cutting-edge sensing techniques and machine learning strategies are being used in smart homes to autonomously respond to the needs of their users; however, they are rooted in the environment.
The RiSH [58] comprises a robot for home service, a sensor network deployed across the home, a sensor network for monitoring body activities, a mobile device, cloud-based servers, and remotely available caregivers. The robot embedded in RISH had the capability to recognize 37 distinct individual activities through sound actions and was able to identify falling sounds with 80% accuracy at the frame level. The study demonstrated the ability of RiSH and the home service robot in observing and supporting the resident. Grguric et al. [65] used artificial intelligence theories of decision making, reasoning, and pattern recognition based on the advances in ambient intelligence (AmI), sensor networks, and human-computer interaction (HCI). The system studies a person's behavior patterns without invading their privacy and signals the caregiver(s) in case of detection of an abnormal situation. Iakovakis et al. [61] used a fuzzy logic-based assistive tool for prevention of falls in patients with Parkinson's disease. The system gathered important signal information from smartwatch and other home-based motion sensors to monitor the risk of fall due to orthostatic hypotension. Rudzicz et al. [70] investigated the use of a mobile robot designed to assist in ADL of elderly people with Alzheimer's disease by monitoring visuals and providing verbal prompts in difficult situations. Fischinger et al. [73] used a robot called 'Hobbit' that assisted the elderly living independently at home. The robotic system adequately performed its core tasks and the subjects were able to perform all tasks with support of the robot. Wilson et al. [46] developed a robot activity support system (RAS) comprised of a sensing network that interacted with the robot; the system detected activity errors in the everyday environment and provided appropriate assistance. For example, it provided physical assistance by locating the key objects required for ADL in the home. Dawadi et al. [48] developed a network of motion and temperature sensors, which scrutinized the daily activities; a machine learning algorithm processed the collected data to compute the task quality, task accuracy, and task sequencing scores. Bianchi et al. [72] proposed a wearable device integrated with deep learning techniques, which recognized most common daily living activities. Saunder et al. [57] used a commercially available robot for their study. They described the teaching and learning method, where the robot is first taught about all the requirements and logistics of a sensor-based house, and once the robot learns it can assist the subjects in their daily living activities. Due to this methodology the robot can easily be customized to meet individual needs, the subjects found this method to be very easy to use and helpful.

Usage Safety, Emergency Services and Fall Detection
Ample importance has been accorded to user safety during the development of smart technologies for home, especially those for monitoring the health of elderly patients with dementia, Alzheimer's, or Parkinson's disease. These patients are more comfortable in their own homes owing to their familiarity with the environment; use of these smart technologies can inculcate a sense of security and alleviate fear and anxiety. These technologies empower these elderly people to recollect their daily tasks (e.g., taking medicine, drinking water, etc.), make them more self-sufficient, reduce their social isolation, and enhance their sense of self-worth. Some studies investigated the use of systems that periodically send reports to caregivers pertaining to the activities carried out by elderly and also notifies in case of any anomaly; however, very few studies have emergency services embedded into the system.
Taramasco et al. [60] embedded an emergency button which can place a call to the caregivers and is also connected to emergency and fire-fighting departments. Tsukiyama [63] deployed a system that assesses the health condition of the elderly and forecasts any emergency situation to a local healthcare center without any explicit user interaction. Fischinger et al. [73] employed a robot which can detect emergency and handle the situation appropriately. Fall detection technology is an essential element of any smart home technology for elderly. Elderly people are more vulnerable to falls due to age-related conditions such as muscle weakness, arthritis, and muscle atrophy. Falls may lead to severe injuries that necessitate medical help.
Deen et al. [44] employed a system which can detect health issues, muscle weakness, and fall through a smart walking monitor and smart joint monitor. Portet et al. [68] used a system with the ability to detect fall and help subjects in calling for help. Do et al. [58] used a robot which was able to detect fall sounds with 80% accuracy. Taramasco et al. [60] incorporated special falling sensors in its tele monitoring ADL platform to detect falls. Iakovakis et al. [61] used a fuzzy logic based assistive tool for fall prevention. Yu et al. [66] used a computer vision-based fall detection system for monitoring an elderly person in home care. Fischinger et al. [73] used a care robot which was able to prevent and detect falls. Gnanavel et al. [53] also include a fall detection system, including a heartbeat sensor, pressure sensor and temperature sensor and alerted the caregivers via SMS in case of any anomaly. Hattink et al. [68] had a surveillance system, which was able to detect inactivity, and was able to predict and alert the caregivers of an emergency situation. Hu et al. [71] was a camera P2P based system which detected falls and alerted the caregivers.

User Feedback, Satisfaction and Effects of Smart Homes
The response of the people towards usage of these smart-systems also seems affirmative [50][51][52]57,71,72]. A study [62] showed improved medication adherence among subjects with use of used water sensors to monitor the usage of water to check maintenance of a healthy lifestyle. However, the positive effects cannot be generalized as these studies were performed with small sample sizes of less than 50; moreover, proxy subjects were used in some cases [58].
A trend observed in the 31 studies was that the deployed smart systems were able to achieve their targeted outcome; moreover, the users rated the systems as sensors to track the medicine intake. Others studied usability [65] and acceptability [88]. However, each of the studies had some limitations and none of the studies replicated a model of a complete smart home. The study by Do et al. [58] used a system that is closest to a complete smart home solution; it includes a home service robot, a home sensor network, a body sensor network, a mobile device, cloud servers, and remote caregivers. The system monitors the ADL, informs the caregivers in case of any anomaly, has a robot at hand to assist in ADL with the ability to recognize 37 daily activities and detect falls. The only limitation was that their technology required the use of a wearable device; as discussed above, wearables are not very comfortable for constant daily use. In addition, there was no fall detection technology used in the bathroom, which is a very high-risk area for falls.

Statistical Analysis
If we observe quantitatively 80% of the studies used some form of sensor embedded in their systems, be they environmental sensors, body sensors, motions sensors, etc. These sensors are the crux of a smart home system as they can monitor and record every move, without hampering inhabitants' daily life, invading their privacy, and also through these sensors, the daily activities can be made efficient and easy to be carried out by subjects with minimum effort. Nearly a fifth (19.3%) of studies used wearables, it was seen that incorporation of wearables in the studies has decreased with time, in recent years, studies are now more focused on sensors and robots to enable a good functional smart home for elderly. Over a third (35.4%) of studies declared usage unobtrusive or privacy-preserving techniques in their methodology, few didn't mention privacy or their unobtrusiveness, even though this is a very low percentage considering this is an important feature, more focus needs to be put in to include privacy and security feature of a smart home, as with advancing technology, there are new ways to breach security and theft of data can be dangerous. Moreover, it puts the subject at ease if they know that the system is secure and protected and their personal data is safe. Nearly 90% (87.09%) of studies have incorporated AI, ML or robots in their smart home research, as these strategies are proving to be more efficient and beneficial, though they are still in their infancy, more research should be put in to incorporate more features and make it more easy to use for the elderly The studies were reviewed were RCTs, pilot studies, experimental studies and also proposed studies. Nearly a fifth (19.3%) were proposed studies, which catered to different subjects like MCI, handicapped and dementia patients or healthy elderly. These studies show a great potential in their research but needs to be validated by including subjects and conducting trials to verify their claim. Few studies open a new window in regard to care of MCI and dementia diagnosed elderly via a smart home strategy. These studies have potential to benefit them immensely in their daily activities, help them live independently, while keeping the caregiver at ease, with emergency and fall detection alert techniques.
To summarize the main features of the smart home technology in the included literature: 80% of the smart home systems reviewed used sensor-based platforms, 29.1% used cameras, 19.3% used wearables, 35.4% used unobtrusive methods, 16.1% used robots, 70.9% used AI and machine learning and 32.2% had fall detection capabilities.

Recommendations for Future Research
There is an inevitable compromise between utility and feasibility. Multiple hardwired installation may be needed at all positions where support could be required (e.g., bathroom, kitchen, and bedroom), which may not be very cost effective. Conversely, installation of too few units may introduce gaps where activity will not be noticed. Integration of robots with smart homes can help with some of these tradeoffs. Moreover, a physically embodied entity like a robot will have greater chances of acceptance than an embedded system [88,89].
Also, robots along with the sensor technology could be a better strategy, as robots can be programmed to assist in ADL (bringing the medicine, reminding of tasks, keeping company, aiding in physical needs like sitting as standing [90], calling the caregivers, etc.), while sensors can be used to monitor the environment. Moreover, there are other determinants of QoL apart from health. Most of the studies employed systems aimed at providing some sort of healthcare.
Satisfaction with and acceptability of any smart home systems is culture-dependent and thus varies in different societies. Age and gender seem to influence people's idea of space [88], which can also affect the acceptability of a system, in particular where behavior is continuously monitored. Identification of the level of user acceptance is a major challenge for system developers [91].
However, as seen the technology readiness for these systems was rated as low. These systems need to incorporate more mechanisms to protect user privacy and data security in order to gain the trust of their users. Apart from monitoring the health status, these systems should incorporate means of entertainment and companionship to ward off loneliness and anxiety. None of the 31 included studies can be considered a complete smart home system, which is unobtrusive, monitors health, has emergency features, helps in ADL together with keeping them motivated and less lonely and less anxious. These measures would provide a happy and positive place to age in place. In addition, more robust safety features should be incorporated in such technologies to achieve a complete smart home solution. Inclusion of entertainment and gaming [91], social companionship [92], and constant support and assurance can help improve the usability and acceptability of the systems, especially among the elderly, especially for those who live independently, as they will be able to keep themselves busy through these features and not feel depressed.
There is a paucity of research on this aspect. None of the 31 studies reviewed had incorporated entertainment as an add-on option in their system, although one study [72] did have the option of video conferencing with friends and family members. Four studies [48,59,71,72] included robots which could provide some sense of companionship to the user. Future research should focus on these aspects along with monitoring the activities and providing healthcare linkage.
To summarize the main features of the smart home technology, out of 31 chosen literature references 27 used environmental sensors, 22 studies used AI or ML techniques in their strategies, 20 systems could monitor activities of daily living, 11 systems used privacy-preserving methods, 10 used fall detection techniques, 9 studies collected feedback from the user, 9 used cameras in their systems, 6 used wearables, 6 used body sensors, 5 involved robot-based methods and 4 used voice commands. Figure 2 depicts this f information in the form of bar graph.
To conclude, a complete smart home should include a strategically designed sensorbased platform which can function with multiple residents in the house; in addition, non-intrusive fall detection sensors should be installed in washrooms. Emergency buttons should be easily accessible to provide ready access to emergency services. Use of cameras and wearables should be minimized. Integration of robot with the system can assist in ADL, provide medication reminders, and inculcate a sense of companionship [83] to alleviate depression and anxiety. Data security and privacy should be accorded highest priority in the development of smart home solutions.

Conclusions
Use of smart-home technology for improving the QoL of older adults has received a generally positive response. The studies included in this review for the most part achieved their target outcomes. However, 50% of the studies pertained to monitoring the ADLs of the subjects and informing their caregivers in case of any abnormalities or discrepancies; the other studies deployed systems to achieve very specific tasks such as checking the medication adherence, monitoring the water flow, and analyzing the walking and sleep patterns.
While most of the studies achieved their objectives, none of the studies can claim to have achieved the objective of implementing a complete smart home. Future studies should incorporate all the key features required in a smart home: individual privacy, monitoring via sensor-based technology, assistance in daily activities via a home robot, provision for connecting to caregivers, access to emergency assistance, and predicting depression.