Title: Facial expression recognition of high jumpers based on wearable multi-physiological parameter collection

Authors: Ruoqun Mou; Ding Lu

Addresses: Department of Physical Education, Yunnan Communications Vocational and Technical College, Kunming 650500, China ' College of Sport, Yunnan University, Kunming 650011, Yunnan, China

Abstract: In order to improve the recognition accuracy of athletes' facial expression, a method of high jump athletes' facial expression recognition based on wearable multi-physiological parameter collection is proposed. Firstly, through the wearable multi-physiological parameter acquisition device, the device is used to collect the high jump athletes' heart rate, skin electrical activity parameters, body temperature and other multi-physiological parameters. Secondly, the baseline drift suppression method based on wavelet transform is used to suppress the baseline drift within multi-physiological parameter signals of high jumpers. Finally, the multi-physiological parameters of high jumpers without baseline drift are inputted into the convolutional neural network model, and the model is used to output the results of high jumpers' facial expression recognition. The experimental results show that the research method can accurately recognise the facial expressions of high jumpers, with the highest recognition accuracy of 99.21%.

Keywords: wearable; acquisition of multiple physiological parameters; high jumper; facial expression recognition; baseline drift; convolutional neural network.

DOI: 10.1504/IJCISTUDIES.2023.132490

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.1/2, pp.33 - 51

Received: 29 Sep 2022
Accepted: 13 Dec 2022
Published online: 24 Jul 2023 *

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