Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning.

ABSTRACT: Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20-50 years), middle-aged (50-70 years], and older adults (70-89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wris

SUBMITTER: Mardini MT 

PROVIDER: S-EPMC8150764 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature