Title: Athlete's facial emotion recognition method based on multi physiological information fusion

Authors: Xingbo Zhou; Junmin Wang

Addresses: Department of Physical Education, Zhangjiakou University, Zhangjiakou 075000, China ' Department of Physical Education, Zhangjiakou University, Zhangjiakou 075000, China

Abstract: In order to overcome the problems of low accuracy and high time consumption of athlete's facial emotion recognition, this paper proposes a method of athlete's facial emotion recognition based on multi physiological information fusion. First of all, a variety of sensors are used to collect the athlete's ECG, respiration, pulse and skin conductance. Secondly, wavelet transform is used to process multiple physiological information. Then, the sequential backward floating selection method is selected to delete redundant features. Finally, combining the physiological information features, the least squares support vector machine is used to output the athlete's facial emotion recognition results. The experimental results show that this method can accurately recognise athletes' facial emotions. The F1 score of facial emotion recognition is higher than 0.97, and the recognition time is less than 300 ms.

Keywords: multiple physiological information; information fusion; facial emotion; least squares support vector machine; wavelet transform.

DOI: 10.1504/IJRIS.2024.138626

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.2, pp.107 - 117

Received: 20 Sep 2022
Accepted: 22 Nov 2022
Published online: 18 May 2024 *

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