Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

ABSTRACT: Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities pe

SUBMITTER: Bai C 

PROVIDER: S-EPMC9032589 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature