PhysioFormer: Integrating multimodal physiological signals and symbolic regression for explainable affective state prediction.

ABSTRACT: As affective computing becomes increasingly crucial in health monitoring and psychological intervention, accurately identifying affective states is a key challenge. While traditional machine learning models have achieved some success in affective computation, their ability to handle complex, multimodal physiological signals is limited. Most affective computing tasks still rely heavily on traditional methods, with few deep learning models applied, particularly in multimodal signal processing. Given the importance of stress monitoring for mental health, developing a highly reliable and accurate affective computing model is essential. In this context, we propose a novel model-PhysioFormer, for affective state prediction using physiological signals. PhysioFormer model integrates individual att

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12578355 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature