Title: Research on emotion recognition of driver's peripheral physiological signals based on LSTM

Authors: Yaqi Fan; Yutong Lin; Xiaolin Cao; Qin Liang; Jing Chen; Wenchao Xu

Addresses: National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, Jilin, China ' National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, Jilin, China ' National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, Jilin, China ' National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, Jilin, China ' National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, Jilin, China ' National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, Jilin, China

Abstract: The development of automotive industry and the increase in car ownership have led to increasingly severe issues regarding driving safety. Intervention to adjust the driver's emotions is important to improve driving safety. Effective emotion recognition is a significant prerequisite and foundation for this. Physiological signals, generated by the activity of the human autonomic nervous system, reflect a person's physical and mental state objectively, can be used as feature signals for emotion recognition. In this paper, three peripheral physiological signals [namely electrocardiograph (ECG), blood volume pulse (BVP) and respiration (RSP)] and subjective evaluation data on driver emotions collected from preliminary experiments were used to construct a driver emotion recognition model using long short-term memory (LSTM). The results indicate that compared to Support Vector Machines (SVM), the driver emotion recognition model with LSTM as the core algorithm performs better in terms of both recognition speed and accuracy.

Keywords: emotion recognition; peripheral physiological signals; driver emotions; long short-term memory; LSTM.

DOI: 10.1504/IJVP.2025.149492

International Journal of Vehicle Performance, 2025 Vol.11 No.4, pp.410 - 425

Received: 20 Nov 2024
Accepted: 03 Jun 2025
Published online: 04 Nov 2025 *

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