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Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning.
- Source :
-
AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2021 Jan 25; Vol. 2020, pp. 803-812. Date of Electronic Publication: 2021 Jan 25 (Print Publication: 2020). - Publication Year :
- 2021
-
Abstract
- Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was: 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.<br /> (©2020 AMIA - All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1942-597X
- Volume :
- 2020
- Database :
- MEDLINE
- Journal :
- AMIA ... Annual Symposium proceedings. AMIA Symposium
- Publication Type :
- Academic Journal
- Accession number :
- 33936455