Towards non-laboratory prediction of relative physical activity intensities from multimodal wearable sensor data

Abstract

This paper explored a non-laboratory approach to effectively predict relative physical activity intensities using regression algorithms on multimodal physiological data. 22 participants completed 5 to 7 physical activity sessions where each session consisted of 5 activity trials ranging from sedentary to vigorous. During the trials, participant's heart rate (HR), r-r interval (RR), electrodermal activity (Eda), and body temperature (Temp) were recorded using wearable sensors. Immediately after each trial, participants provided their rating of perceived effort (RPE) using the 6-20 Borg scale. This work used both person-level features and features extracted from each of the sensor modality; followed by a feature selection step. Then, using leave-one-subject-out cross-validation, two regression algorithms including linear regression, and support vector machine regression were applied separately on each modality features and all possible modality features combinations. The results showed that both regression algorithms produced similar accuracy. In terms of the usefulness of a single modality, features extracted from RR provided highest prediction performance compared to any other single modality. However, combination of Eda and Temp features fused with RR features produced the best overall performance, confirming the benefits of using multi-modal data.