Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking

Ethics and Acceptance of Smart Homes for Older Adults

Societal challenges associated with caring for the physical and mental health of the elderly worldwide have grown at an unprecedented pace, increasing demand for healthcare services and technologies [1]. Despite the development of several assistive systems tailored to older adults, the rate of adoption of health technologies is low [2, 3]. This review discusses the ethical and acceptability challenges resulting in low adoption of health technologies specifically focused on smart homes for the elderly. The findings have been structured in two categories: Ethical Considerations (Privacy, Social Support, Autonomy) and Technology Aspects (User Context, Usability, Training). The findings conclude that the elderly community is more likely to adopt assistive systems when four key criteria are met. The technology should: be personalized towards their needs, protect their dignity and independence, provide user control, and not be isolating. Finally, we recommend researchers and developers working on assistive systems to: (1) Provide interfaces via smart devices to control and configure the monitoring system with feedback for the user, (2) Include various sensors/devices to architect a smart home solution in a way that is easy to integrate in daily life and (3) Define policies about data ownership

Contactless Gait Assessment in Home-like Environments

Gait analysis is an important part of assessments for a variety of health conditions, specifically neurodegenerative diseases. Currently, most methods for gait assessment are based on manual scoring of certain tasks or restrictive technologies. We present an unobtrusive sensor system based on light detection and ranging sensor technology for use in home-like environments. In our evaluation, we compared six different gait parameters, based on recordings from 25 different people performing eight different walks each, resulting in 200 unique measurements. We compared the proposed sensor system against two state-of-the art technologies, a pressure mat and a set of inertial measurement unit sensors. In addition to test usability and long-term measurement, multi-hour recordings were conducted. Our evaluation showed very high correlation (r>0.95) with the gold standards across all assessed gait parameters except for cycle time (r=0.91). Similarly, the coefficient of determination was high (R2>0.9) for all gait parameters except cycle time. The highest correlation was achieved for stride length and velocity (r≥0.98,R2≥0.95). Furthermore, the multi-hour recordings did not show the systematic drift of measurements over time. Overall, the unobtrusive gait measurement system allows for contactless, highly accurate long- and short-term assessments of gait in home-like environments

“That’s for old so and so’s!”: does identity influence older adults’ technology adoption decisions?

The role of identity in older adults’ decision-making about assistive technology adoption has been suggested but not fully explored. This scoping review was conducted to better understand how older adults’ self-image and their desire to maintain this, influences their decision-making processes regarding assistive technology adoption. Using the five-stage scoping review framework by Arksey and O’Malley, a total of 416 search combinations were run across 9 databases, resulting in a final yield of 49 articles. From these 49 articles, five themes emerged: (1) Resisting the negative reality of an ageing and/or disabled identity; (2) Independence and control are key; (3) The aesthetic dimension of usability; (4) Assistive technology as a last resort; and (5) Privacy versus pragmatics. The findings highlight the importance of older adults’ desire to portray an identity consistent with independence, self-reliance and competence, and how this desire directly impacts their assistive technology decision-making adoption patterns. These findings aim to support the adoption of assistive technologies by older adults to facilitate engagement in meaningful activities, enable social participation within the community, and promote health and well-being in later life