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


Currently, oxygen uptake (VO2) is the most precise means of investigating aerobic fitness and level of physical activity; however, VO2 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 VO2 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 VO2 dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method. The VO2 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 VO2 during ADL was strongly correlated (r = 0.87, P < 0.001) with the measured VO2 and the prediction bias was 0.2 ml˙min-1 ˙kg-1. The MNG calculated based on predicted VO2 was strongly correlated (r = 0.71, P < 0.001) with MNG calculated based on measured VO2 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.

Scientific Reports