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Literature review on monitoring technologies and their outcomes in independently living elderly people
Abstract
Purpose: To obtain insight into what kind of monitoring technologies exist to monitor activity in-home, what the characteristics and aims of applying these technologies are, what kind of research has been conducted on their effects and what kind of outcomes are reported. Methods: A systematic document search was conducted within the scientific databases Pubmed, Embase, Cochrane, PsycINFO and Cinahl, complemented by Google Scholar. Documents were included in this review if they reported on monitoring technologies that detect activities of daily living (ADL) or significant events, e.g. falls, of elderly people in-home, with the aim of prolonging independent living. Results: Five main types of monitoring technologies were identified: PIR motion sensors, body-worn sensors, pressure sensors, video monitoring and sound recognition. In addition, multicomponent technologies and smart home technologies were identified. Research into the use of monitoring technologies is widespread, but in its infancy, consisting mainly of small-scale studies and including few longitudinal studies. Conclusions: Monitoring technology is a promising field, with applications to the long-term care of elderly persons. However, monitoring technologies have to be brought to the next level, with longitudinal studies that evaluate their (cost-) effectiveness to demonstrate the potential to prolong independent living of elderly persons.Implications for RehabilitationInsight was obtained of what kind of technologies exist to monitor activity of daily living, what their functionalities and outcomes of using these technologies are to prolong independent living of non-institutionalised elderly people.Five main groups of monitoring technologies exist with a wide range of functionalities. Research into the use of monitoring technologies is widespread but in its infancy, consisting mainly of small-scale studies and including few longitudinal studies.Research into the use of monitoring technologies demonstrated that the systems are able to monitor daily activities and have the potential of prolonging independent living of elderly people.
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Cites
Portable preimpact fall detector with inertial sensors
Wu, G., & Xue, S. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2007.916282 · Cited by 227
Unobtrusive assessment of activity patterns associated with mild cognitive impairment
Hayes, T. L., Abendroth, F., Adami, A., Pavel, M., Zitzelberger, T. A., & Kaye, J. A. (2008) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2008.07.004 · Cited by 187
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Older people, assistive technologies, and the barriers to adoption: A systematic review
Yusif, S., Soar, J., & Hafeez-Baig, A. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.07.004 · Cited by 548
IoT Wearable Sensors and Devices in Elderly Care: A Literature Review
Stavropoulos, T. G., Papastergiou, A., Mpaltadoros, L., Nikolopoulos, S., & Kompatsiaris, I. (2020) · Sensors · doi:10.3390/s20102826 · Cited by 348
A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone
Wang, A., Chen, G., Yang, J., Zhao, S., & Chang, C.-Y. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2545708 · Cited by 324
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Metadata
- Authors
- Peetoom, K. K. B., Lexis, M. A. S., Joore, M., Dirksen, C. D., & De Witte, L. P.
- Venue
- Disability and Rehabilitation: Assistive Technology
- Publisher
- Informa UK Limited
- Published
- 2014-09-24
- Volume
- 10
- Issue
- 4
- Pages
- 271-294
- ISSN
- 1748-3107 (ISSN-L)
- 1748-3115
- OpenAlex
- W2166595512
- Cited by
- 208
- References
- 2
- FWCI
- 13.65
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Peetoom, K. K. B., Lexis, M. A. S., Joore, M., Dirksen, C. D., & De Witte, L. P. (2015). Literature review on monitoring technologies and their outcomes in independently living elderly people. Disability and Rehabilitation: Assistive Technology. https://doi.org/10.3109/17483107.2014.961179
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.