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Abnormal Emotion Detection of Tennis Players by Using Physiological Signal and Mobile Computing

Abnormal Emotion Detection of Tennis Players by Using Physiological Signal and Mobile Computing

Xiaoyan Sun
Copyright: © 2022 |Volume: 13 |Issue: 3 |Pages: 14
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781683181712|DOI: 10.4018/IJISMD.300779
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MLA

Sun, Xiaoyan. "Abnormal Emotion Detection of Tennis Players by Using Physiological Signal and Mobile Computing." IJISMD vol.13, no.3 2022: pp.1-14. http://doi.org/10.4018/IJISMD.300779

APA

Sun, X. (2022). Abnormal Emotion Detection of Tennis Players by Using Physiological Signal and Mobile Computing. International Journal of Information System Modeling and Design (IJISMD), 13(3), 1-14. http://doi.org/10.4018/IJISMD.300779

Chicago

Sun, Xiaoyan. "Abnormal Emotion Detection of Tennis Players by Using Physiological Signal and Mobile Computing," International Journal of Information System Modeling and Design (IJISMD) 13, no.3: 1-14. http://doi.org/10.4018/IJISMD.300779

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Abstract

Emotion is an important research topic in the field of sports. The physiological changes caused by emotion have a great influence on the completion of sports. It cannot only fully mobilize the organism and maximize the exercise potential, but also lead to muscle stiffness, movement deformation, or muscle contraction weakness. Furthermore, it can affect the completion of exercise. In order to ensure the athlete can keep the best competitive level, it is necessary to estimate the athlete's emotion before competition. This paper adopts the pulse wave signal to implement the emotion estimation for the athletes. First, the pulse wave signals are collected by using a portable sensor via mobile computing. Then, the collected pulse wave signals have noises removed by wavelet transform. Last, the denoised pulse wave signals are represented as the features in time domain and frequency domain to input into a trained classifier for determining the current emotion status. The experimental results show that the proposed method can recognize more than 90% of the abnormal emotions.