Health and wellness monitoring using ambient sensor networks
Journal of Ambient Intelligence and Smart Environments, ISSN: 1876-1364, Vol: 12, Issue: 2, Page: 139-151
2020
- 13Citations
- 20Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Smart homes equipped with ambient wireless sensor networks provide new opportunities to help older adults age-in-place, improve their quality of life and help better manage their health and wellness. In this paper, we present a methodology that estimates occupants' status as active, sedentary, in-bed, out-of-home and unobservable, their location in the house, and their daily activities related to overall health and wellness. The methodology is used to visualize and examine the daily patterns and activities of older adults living in their own homes and participating in a smart home research project. The proposed location and status estimation algorithm is highly accurate as validated by a mobile app that prompts participants with questions about the estimated time of their daily activities. A case study involving a significant health-related life event is presented where the participant's account of changes in her patterns and activities through bi-weekly interviews are shown to confirm inferences based on the results of the proposed methodology.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85082392467&origin=inward; http://dx.doi.org/10.3233/ais-200553; https://journals.sagepub.com/doi/full/10.3233/AIS-200553; https://dx.doi.org/10.3233/ais-200553; https://content.iospress.com:443/articles/journal-of-ambient-intelligence-and-smart-environments/ais200553
SAGE Publications
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know