Work

Multimodal physiological signals from wearable sensors for affective computing: A systematic review

Year: 2025

Type: review

Abstract: In recent years, advances in wearable devices have driven the rapid development of multimodal physiological signals for affective computing. In this paper, we systematically review 34 relevant studies between 2015 and 2025 following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, focusing on the combination of multimodal wearable sensing with learning models. We find that multimodal measurements have become increasingly common in the field, with electrodermal activity (EDA) and electrocardiograms (ECGs) being the most commonly used signal modalities. Several widely used public datasets, including WESAD, DAPPER, and DEAP, provide a basis for model optimization and comparative experiments. Deep learning methods have been widely adopted, with convolutional neural networks (CNNs) and long short-term memory (LSTM) being the most commonly used, and gradually extending to architectures such as the transformer. Despite recent advancements in multimodal emotion recognition studies, the field still faces key challenges, including the lack of long-term continuous data in naturalistic settings, the limited adaptability of current models to multimodalities, and the limited generalizability and interpretability of models for real-world applications. Future research needs to continue to address data acquisition, method optimization, and model generalizability in applications.

Source: Intelligent Sports and Health

DOI: 10.1016/j.ish.2025.10.001

Authors: Fang Li, Dan Zhang

Institution: Tsinghua University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: