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 prov...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/3/864 |
| _version_ | 1797406347579359232 |
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| author | Ju Wang Nicolai Spicher Joana M. Warnecke Mostafa Haghi Jonas Schwartze Thomas M. Deserno |
| author_facet | Ju Wang Nicolai Spicher Joana M. Warnecke Mostafa Haghi Jonas Schwartze Thomas M. Deserno |
| author_sort | Ju Wang |
| collection | DOAJ |
| description | 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 <inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>55</mn></mrow></semantics></math></inline-formula> 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 (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>38</mn></mrow></semantics></math></inline-formula>), time spent on activities (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>18</mn></mrow></semantics></math></inline-formula>)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>5</mn></mrow></semantics></math></inline-formula>). 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. |
| first_indexed | 2024-03-09T03:25:06Z |
| format | Article |
| id | doaj.art-0f486d800938479f8541218b10922e8e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:25:06Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0f486d800938479f8541218b10922e8e2023-12-03T15:03:11ZengMDPI AGSensors1424-82202021-01-0121386410.3390/s21030864Unobtrusive Health Monitoring in Private Spaces: The Smart HomeJu Wang0Nicolai Spicher1Joana M. Warnecke2Mostafa Haghi3Jonas Schwartze4Thomas M. Deserno5Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, GermanyPeter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, GermanyPeter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, GermanyPeter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, GermanyPeter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, GermanyPeter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Muehlenpfordtstr. 23, D-38106 Braunschweig, Lower Saxony, GermanyWith 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 <inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>55</mn></mrow></semantics></math></inline-formula> 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 (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>38</mn></mrow></semantics></math></inline-formula>), time spent on activities (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>18</mn></mrow></semantics></math></inline-formula>)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>5</mn></mrow></semantics></math></inline-formula>). 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.https://www.mdpi.com/1424-8220/21/3/864sensorsmart homehealth monitoringelderlypatientambient assisted living |
| spellingShingle | Ju Wang Nicolai Spicher Joana M. Warnecke Mostafa Haghi Jonas Schwartze Thomas M. Deserno Unobtrusive Health Monitoring in Private Spaces: The Smart Home Sensors sensor smart home health monitoring elderly patient ambient assisted living |
| title | Unobtrusive Health Monitoring in Private Spaces: The Smart Home |
| title_full | Unobtrusive Health Monitoring in Private Spaces: The Smart Home |
| title_fullStr | Unobtrusive Health Monitoring in Private Spaces: The Smart Home |
| title_full_unstemmed | Unobtrusive Health Monitoring in Private Spaces: The Smart Home |
| title_short | Unobtrusive Health Monitoring in Private Spaces: The Smart Home |
| title_sort | unobtrusive health monitoring in private spaces the smart home |
| topic | sensor smart home health monitoring elderly patient ambient assisted living |
| url | https://www.mdpi.com/1424-8220/21/3/864 |
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