A Multi-layered Deep Learning Approach for Human Stress Detection
Book Chapter
Soni, J, Prabakar, N, Upadhyay, H. (2023). A Multi-layered Deep Learning Approach for Human Stress Detection
. 13741 LNCS 7-17. 10.1007/978-3-031-27199-1_2
Soni, J, Prabakar, N, Upadhyay, H. (2023). A Multi-layered Deep Learning Approach for Human Stress Detection
. 13741 LNCS 7-17. 10.1007/978-3-031-27199-1_2
In today's fast-paced world, stress is common on various occasions in everyday life. However, long-term stress hinders normal lives. Detection of such mental stress at an earlier stage can prevent many associated health problems. There are significant changes in the multiple bio-signals, such as electrical, thermal, optical, etc., when an individual is under stress. Such bio-signals can be utilized to identify stress. In this paper, we propose a multi-layered deep learning-based approach for detecting human stress using the multimodal dataset. We use an open-source dataset, namely Wearable Stress and Affect Detection (WESAD), which contains data from wearable physiological and motion sensors. The modalities of these sensors include axis acceleration, body temperature, electrocardiogram, and electrodermal activity with three conditions: baseline, amusement, and stress. In the first layer of our multi-layered approach, we train and compare AutoEncoder and Variational AutoEncoder to learn the normal emotional state of the subject. In the second layer, we train and compare LSTM and Transformer models to classify the subjects as either in an amused or stressed state. This multi-layered approach helps to achieve a higher stress detection rate of 98%.
