Contrastive Pretraining for Stress Detection with Multimodal Wearable Sensor Data and Surveys.

Abstract

Stress adversely affects mental and physical health and underscores the importance of early detection. Some studies have utilized physiological signals from wearable sensors and other information to monitor stress levels in daily life. Recent studies use self-supervised methods due to the high cost of collecting stress labels. However, self-supervised learning using both time series and tabular features such as demographics, traits, and contextual information has been understudied. Therefore, there is a need to further investigate how a model can be effectively trained with different granularity of multimodal data and a limited number of labels. In this study, we introduce a self-supervised multimodal learning approach for stress detection that combines time series and tabular features. Our proposed method presents a promising solution for effectively monitoring stress using multimodal data.