Characterizing daily physical activity patterns with unsupervised learning via functional mixture models

Ensari, I., Caceres, B. A., Jackman, K. B., Goldsmith, J., Suero-Tejeda, N. M., Odlum, M. L., & Bakken, S. (2024). Characterizing daily physical activity patterns with unsupervised learning via functional mixture models. Journal of Behavioral Medicine. https://doi.org/10.1007/s10865-024-00519-w
Authors:
Ipek Ensari
Billy A Caceres
Kasey B Jackman
Jeff Goldsmith
Niurka M Suero-Tejeda
Michelle L Odlum
Suzanne Bakken
Affiliated Authors:
Ipek Ensari
Billy A Caceres
Kasey B Jackman
Jeff Goldsmith
Niurka M Suero-Tejeda
Michelle L Odlum
Suzanne Bakken
Columbia Affiliation:
Author Keywords:
accelerometry
clustering
functional data
physical activity
unsupervised learning
adult
aged
bayes theorem
cluster analysis
exercise
female
human
male
middle aged
psychology
sedentary lifestyle
statistical model
unsupervised machine learning
young adult
humans
models, statistical
sedentary behavior
Grants:
K01HL146965 (NIH – National Heart, Lung, and Blood Institute)
P30NR016587 (NIH – National Institute of Nursing Research)
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Publication Type:
Article
Unique ID:
10.1007/s10865-024-00519-w
PMID:
39306631
DOI:
10.1007/s10865-024-00519-w
Journal:
Journal of Behavioral Medicine
Publication Date:
Data Source:
PubMed
Source Link:
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