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Unobtrusive Health Monitoring in Private Spaces: The Smart Home

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Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, Germany
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Wohnungsentwicklung und Forschung, Nibelungen-Wohnbau-GmbH, Freyastr. 10, D-38106 Braunschweig, Lower Saxony, Germany
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Author to whom correspondence should be addressed.
Academic Editor: Anthony Fleury
Sensors 2021, 21(3), 864; https://doi.org/10.3390/s21030864
Received: 2 December 2020 / Revised: 8 January 2021 / Accepted: 23 January 2021 / Published: 28 January 2021
(This article belongs to the Special Issue Simplified Sensing for Ambient Assisted Living in Smart Homes)
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. View Full-Text
Keywords: sensor; smart home; health monitoring; elderly; patient; ambient assisted living sensor; smart home; health monitoring; elderly; patient; ambient assisted living
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MDPI and ACS Style

Wang, J.; Spicher, N.; Warnecke, J.M.; Haghi, M.; Schwartze, J.; Deserno, T.M. Unobtrusive Health Monitoring in Private Spaces: The Smart Home. Sensors 2021, 21, 864. https://doi.org/10.3390/s21030864

AMA Style

Wang J, Spicher N, Warnecke JM, Haghi M, Schwartze J, Deserno TM. Unobtrusive Health Monitoring in Private Spaces: The Smart Home. Sensors. 2021; 21(3):864. https://doi.org/10.3390/s21030864

Chicago/Turabian Style

Wang, Ju, Nicolai Spicher, Joana M. Warnecke, Mostafa Haghi, Jonas Schwartze, and Thomas M. Deserno 2021. "Unobtrusive Health Monitoring in Private Spaces: The Smart Home" Sensors 21, no. 3: 864. https://doi.org/10.3390/s21030864

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