Stress has significant effects on an individual's daily life in modern society, making its detection a topic of great interest over the decade. While numerous studies have delved into this field, the accuracy and reliability of stress detection methods still have room for improvement. In this study, we propose a multimodal multitemporal-scale fusion-based stress detection system. First, a hybrid feature extraction module is proposed, which generates a feature set from the perspective of handcrafted and deep learning (DL) analysis across multiple temporal scales. Second, a stress detection modu...
