eFurniture for home-based frailty detection using artificial neural networks and wireless sensors
Introduction
Frailty is one of the greatest gerontological challenges faced by modern societies with aging populations. Compared with non-frail persons of similar age, frail elderly people have a much higher risk of suffering related injuries, which could result in disability, hospitalization, institutionalization, or even death. For this reason, early detection of frailty could help delay the onset of frailty and reduce its adverse outcomes. There are several existing frailty measurement methods [1], [2], [3], [4], however, results are often confounded by experimenters’ bias and the inconvenience imposed on subjects [5], [6].
Aging frailty generally occurs in the elderly population and is clinically defined as a state of decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiological systems. Frailty increases the risk of adverse health outcomes, including mortality, institutionalization, falls and hospitalization [7], [8], [9]. In the United States and Europe, approximately 10–25% of the elderly population over the age of 65 and almost 50% of population over the age of 85 meet the definition of frailty [10]. Depending on the measurement protocol used, the estimated prevalence will vary, generally from 6.9 to 63%. Frailty will be one of the greatest gerontological challenges faced by societies with aging populations in the next 10 years [11]. Fortunately, aging frailty can be prevented and corrected [12], [13]. However, to prevent frailty, physicians need to be able to identify the individuals at risk of frailty before they become frail. Currently there is a lack of effective methods and devices to monitor physical degradation in elderly people over time. Without such tools, it is not possible to help elderly people avoid or mitigate frailty problems.
To slow down the aging rate of the elderly and ensure a healthy lifestyle, the following factors need to be taken into consideration: (1) establishing a body function degeneration analysis mechanism. There has never been a standard definition of aging. To help the elderly understand the status of their body's degeneration, this study attempts to quantify aging standards, and establish an analysis mechanism. (2) Developing a long-term recording and tracking device. In the past, many degenerative indicators have to be measures under the supervision of examiners. The subjects are not able to conduct the tests themselves. In addition, the measurements are easily affected by time and space limitations that in turn cause difficulties in recording long-term aging trends. This study proposes a novel electronic measurement method to improve the accuracy and validity of the data measured by a variety of instruments and equipment. The measurements are used to assist the elderly to establish a personalized body degeneration database.
Section snippets
Methods
This study describes an automatic system to measure general clinical indicators and to assist elderly people to build personal health records [14], [15]. As shown in Fig. 1, the system architecture consists of two parts. (1) A suite of measurement instruments for frailty indicators, including an eScale, an eChair, an ePad, an eReach and electronic questionnaires. We based our system on existing measurement methods, and have integrated similar measurement methods into the electronic measurement
Results
The experiment uses real case data to train the classification model. We attempt to establish an optimized aging estimation model by tuning different network parameters. The cases used in this study were collected from volunteers in Chang Gung Hospital and Rehabilitation center. The degeneration information of 309 people over 65 years of age was collected in this study, 178 of them female and 131 male. With the same ratio between females and males, we randomly selected 160 people as subjects to
Conclusion
Various electronic measuring components were developed in the study to target different aging indicators. The eScale includes a home lighting feature. The eChair is combined with a chair. The ePad employs a software membrane sensor and is integrated in the carpet. The home-based concept is fused into all designs to provide home-based measurement and solve the problem that health checks are limited to taking place in health institutions, as noted above.
Identification of degeneration level is not
Conflict of interest
None.
Acknowledgment
The authors would like to thank the National Science Council, Taiwan, R.O.C., for supporting this research under grant NSC-99-2218-E-182-001.
References (16)
- et al.
A pervasive health monitoring service system based on ubiquitous network technology
International Journal of Medical Informatics
(2008) - et al.
A global clinical measure of fitness and frailty in elderly people
Canadian Medical Association Journal
(2005) - et al.
Conceptualizations of frailty in relation to older adults
Journal of Advanced Nursing
(2003) - et al.
Interventions to prevent disability in frail community-dwelling elderly: a systematic review
BMC Health Services Research
(2008) - et al.
The association between obesity and the frailty syndrome in older women: the women's health and aging studies
Journal of the American Geriatrics Society
(2005) - et al.
Moving research into practice: a decision framework for integrating home telehealth into chronic illness care
International Journal of Medical Informatics
(2006) - et al.
Frailty in older adults: evidence for a phenotype
The Journals of Gerontology
(2001) - et al.
Targeting frail older adults for outpatient comprehensive geriatric assessment and management services: an overview of concepts and criteria
Reviews in Clinical Gerontology
(2002)
Cited by (26)
Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection
2021, Ageing Research ReviewsCitation Excerpt :For instance, a light-emitting diode (LED) screen and a wireless sensor module inside a lamp for measuring reaction time and speed of the movements or a chair with pressure sensors for measuring weakness and weight (Chang et al., 2013). Another tool is based on several pads hidden under the carpet for gait and balance control (Chang et al., 2013) (Fig. 10). The tools described above are based on standard furniture improved with sensors, but it is also possible to add new furniture or devices to analyse different phenotypes.
Digital Health Interventions among People Living with Frailty: A Scoping Review
2021, Journal of the American Medical Directors AssociationCitation Excerpt :RCT studies that showed good efficacy were an exercise program based on a game system,29,30 another exercise program in a tablet and a night pad light to prevent falls.31 Among cross-sectional studies, DHI that showed good frailty prediction or efficacy were a set of e-furniture (frailty assessment),32 a balance quality tester (falls),33 and a single wrist sensor (frailty detection).34 A longitudinal study that showed good efficacy was a DHI with a light path for preventing falls35 (Table 2).
E-health for active ageing; A systematic review
2018, MaturitasA novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks, based on the Internet of Things notion
2014, Ad Hoc NetworksCitation Excerpt :At that time the WSN research papers focused only on the theoretical and broad applicative uses of WSNs (e.g. military, environment, healthcare), but the research and applicative use of WSN has significantly increased over the last few years. Today we talk about the use of WSN for traffic monitoring [3], pipeline monitoring [4], landslide detection [5], methane leak detection [6], border patrol [7], precision agriculture [8], rehabilitation applications [9], laboratory tutoring [10], asset tracking [11], real-time soccer playing monitoring [12] and many more [13–16]. A list of real-life applicative WSN projects was summed up by Romer and Mattern [2].
Real-Time Water Quality Assessment via IoT: Monitoring pH, TDS, Temperature, and Turbidity
2023, Ingenierie des Systemes d'InformationArchitecture of a Non-Intrusive IoT System for Frailty Detection in Older People
2023, Electronics (Switzerland)