Description
Periodicities (repeating patterns) are observed in many human behaviors. Their strength may capture untapped patterns that incorporate sleep, sedentary, and active behaviors into a single metric indicative of better health. We present a framework to detect periodicities from longitudinal wrist-worn accelerometry data.
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Contributors
- Buman, Matthew (Author)
- Hu, Feiyan (Author)
- Newman, Eamonn (Author)
- Smeaton, Alan F. (Author)
- Epstein, Dana R. (Author)
- College of Health Solutions (Contributor)
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2016-01-04
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Identifier
- Digital object identifier: 10.1155/2016/4856506
- Identifier TypeInternational standard serial numberIdentifier Value2314-6133
- Identifier TypeInternational standard serial numberIdentifier Value2314-6141
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- The article is published at https://www.hindawi.com/journals/bmri/2016/4856506/, opens in a new window
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Buman, M. P., Hu, F., Newman, E., Smeaton, A. F., & Epstein, D. R. (2016). Behavioral Periodicity Detection from 24 h Wrist Accelerometry and Associations with Cardiometabolic Risk and Health-Related Quality of Life. BioMed Research International, 2016, 1-9. doi:10.1155/2016/4856506