Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting
Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.
Authors
- Jyotirmoy Nirupam Das (ORCID: https://orcid.org/0000-0003-4068-3988)
- Soundar Kumara (ORCID: https://orcid.org/0000-0002-7941-8818)
- Linying Ji (ORCID: https://orcid.org/0000-0003-1908-3718)
- Sy‐Miin Chow (ORCID: https://orcid.org/0000-0003-1938-027X)
- Orfeu Marcello Buxton (ORCID: https://orcid.org/0000-0001-5057-633X)
- Yuqi Shen (ORCID: https://orcid.org/0009-0008-9114-8743)
Institutions
- Pennsylvania State University (US)
- Montana State University (US)
Publication Details
- Journal
- Sleep Health
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1016/j.sleh.2024.10.003
- Citations
- 2
- Primary Topic
- Physical Activity and Health
- Type
- article
- Field-Weighted Citation Impact
- 2.33
Funders
- National Science Foundation
- Alfred P. Sloan Foundation
- Boston College
- Small Business Innovation Research
- University of Pennsylvania
- Pennsylvania State University
- National Institutes of Health
- Centers for Disease Control and Prevention
- Social Science Research Institute, Pennsylvania State University
- National Heart, Lung, and Blood Institute
- National Institute of Child Health and Human Development
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development