Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

ABSTRACT: Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o₂) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o₂ kinetics). This study evaluated aerobic system dynamics based on predicted V̇o₂ data obtained from wearable sensors during unsupervised activities of daily living (μADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (μADL data). Variables derived from hip accelerometer (ACC_HIP<

SUBMITTER: Beltrame T 

PROVIDER: S-EPMC5867367 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature