Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living

Abstract: Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the  dynamics during ADL. The variables derived from the wearable sensors were used to create a  predictor based on a random forest method. The  temporal dynamics were assessed by the mean normalized gain amplitude (MNG) obtained from frequency domain analysis. The MNG provides a means to assess aerobic fitness. The predicted  during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured  and the prediction bias was 0.2 ml·min−1·kg−1. The MNG calculated based on predicted  was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured  data. This new technology provides an important advance in ambulatory and continuous assessment of aerobic fitness with potential for future applications such as the early detection of deterioration of physical health.