Title: Rapid recognition of athlete's anxiety emotion based on multimodal fusion

Authors: Li Wang

Addresses: Anhui Sanlian University, Hefei, 230601, Anhui, China; University of Perpetual Help System DALTA, Las Pinas City, 1740, Philippines

Abstract: The diversity of anxiety emotions and individual differences among different athletes have increased the difficulty of emotion recognition. To address this, a rapid recognition method of athlete's anxiety emotion based on multimodal fusion is proposed. Wireless sensor networks are used to collect facial expression images of athletes, and wavelet transform is applied for denoising the collected images. Image features are extracted using grey-level co-occurrence matrix, and the athlete's facial expression images are normalised. Features related to the athlete's emotions, such as voice characteristics, facial expression features, and physiological indicators, are obtained. These features from different perceptual modalities are fused to achieve rapid recognition of athletes' anxiety emotions. The test results demonstrate that this method not only improves the image denoising effect but also achieves high accuracy and efficiency in emotion recognition, enabling accurate and real-time recognition of athletes' emotions.

Keywords: multimodal fusion; rapid recognition; wireless sensor networks; wavelet transform.

DOI: 10.1504/IJBM.2024.140770

International Journal of Biometrics, 2024 Vol.16 No.5, pp.449 - 462

Received: 30 Jun 2023
Accepted: 14 Sep 2023
Published online: 02 Sep 2024 *

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