HealthGate: unobtrusive home monitoring of vital signs, weight and mobility of the elderly
Authors: Närväinen Johanna , Kortelainen Juha , Urhemaa Timo , Saajanlehto Mikko , Bäckman Kari , Plomp Johan 
Published: June 2023

This paper will discuss the feasibility of a monitoring setup HealthGate, designed to monitor the mobility, vital signs, and weight of an elderly person living in her own apartment. The versatile sensor setup will allow more comprehensive insights than what is currently available. Continuous home monitoring will enable early interventions and actions in e.g. suspected dehydration, mobility problems, and non-optimal or missed medication. The data can be used to form indices of e.g. frailty and sleep quality, to detect changes in health and behavior, and to alert the person, relatives or caregivers of detected and impending problems. Instead of interaction with the user, the setup seeks total unobtrusiveness: invisible or integrated sensors as well as automated measurements and data transmission. This is crucial with persons suffering from severe cognitive impairment: the operation does not rely on user actions and the setup is safe from a curious user. On the other hand, tailored reports can be provided to people who can and want to investigate their own status. The custom-made monitoring system uses three sensor types: a mm-range imaging FMCW radar (1), a seat foil sensor (2), and a novel four-element weight sensor array. The seat and weight sensors are positioned in a favorite armchair and the radar cabinet faces the chair, typically positioned next to the TV. The key events from which the data are recorded are the transitions to and from the chair and the moments sitting still in the in, typically watching TV. The system will monitor heart and breathing rate (both radar and seat foil), weight, and dynamic weight distribution across the sensors under the legs of the chair, as well as movement at and near the chair (radar). Sleep is monitored using a commercial sleep sensor (VTracker 2.0, eLive Ecosystem Ltd., Finland) placed underneath the topping mattress. As the chairs used in individual homes will vary making inter-subject comparisons more difficult, during each home monitoring period, the participants will also perform a guided sitting, standing-up and walking protocol using a similar setup but with a test chair. The 25 participants are residents of a senior community, living independently in their rental apartments but using home care services. The data are collected during a series of two two-week monitoring periods, five participants at a time, starting in November 2022. We will describe the setup and data collection solution as well as show the first multisensor data comparisons and the proposals for characteristic mobility parameters for a sit down - stand up sequence and walk. The quality, reliability and limits of the biosignals and movement parameters derived from the radar data will be discussed. The data will be compared to standard measures of frailty, collected in a controlled test session, consisting of grip force, walking speed, timed sit down – stand up, and agility tests, as well as the frailty index (3) computed from the interRAI-HC assessments collected bi-annually. The daily patterns, biosignal data and daily weight variation will be compared against sleep data and interview data on acute illnesses and other conditions influencing behavior and well-being. Finally, the usability and acceptability of the setup are discussed, based on the interview data collected from the participants and home care nurses.(1) M. Mercuri et al., (2016). Biomedical wireless radar sensor network for indoor emergency situations detection and vital signs monitoring. IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS), pp. 32-35(2) Anttonen, J., & Surakka, V. (2005, April). Emotions and heart rate while sitting on a chair. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 491-499).(3) Faller JW, et al. (2019) Instruments for the detection of frailty syndrome in older adults: A systematic review. PLOS ONE 14(4): e0216166