Gated Recurrent Unit Network for Psychological Stress Classification Using Electrocardiograms from Wearable Devices

In recent years, research on human psychological stress using wearable devices has gradually attracted attention. However, the physical and psychological differences among individuals and the high cost of data collection are the main challenges for further research on this problem. In this work, our aim is to build a model to detect subjects' psychological stress in different states through electrocardiogram (ECG) signals. Therefore, we design a VR high-altitude experiment to induce psychological stress for the subject to obtain the ECG signal dataset. In the experiment, participants wear smart ECG T-shirts with embedded sensors to complete different tasks so as to record their ECG signals synchronously. Considering the temporal continuity of individual psychological stress, a deep, gated recurrent unit (GRU) neural network is developed to capture the mapping relationship between subjects' ECG signals and stress in different states through heart rate variability features at different moments, so as to build a neural network model from the ECG signal to psychological stress detection. The experimental results show that compared with all comparison methods, our method has the best classification performance on the four stress states of resting, VR scene adaptation, VR task and recovery, and it can be a remote stress monitoring solution for some special industries.