Open Access Paper
12 November 2024 Automatic recognition method of abnormal behavior in athletes
Yang Zou, Ying Bao, Xuejie Zhong, Min Li
Author Affiliations +
Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 1339539 (2024) https://doi.org/10.1117/12.3048544
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
This paper presents an automatic recognition method for abnormal behaviors in exercising human bodies, including sample image collection and preprocessing. The displacement of shoulder, elbow, wrist, and knee joints of the exercising human body are taken as state variables. A normal exercising human body form observer is designed based on Kalman prediction, along with the design of observation gain for the exercising human body form observer. A threshold for determining abnormal behaviors in exercising human bodies is set. Another set of exercising human body samples, Sample II, is used to test the designed exercising human body form observer. The main advantage of this method lies in its ability to establish a database through the collection of human movements, to predict subsequent movements, and to preemptively determine the trend of any abnormal behaviors so that intervention control can be applied in advance if necessary. Additionally, the robustness of recognition is enhanced through image enhancement, image filtering noise reduction, morphological analysis of images, and edge detection processes in data.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yang Zou, Ying Bao, Xuejie Zhong, and Min Li "Automatic recognition method of abnormal behavior in athletes", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 1339539 (12 November 2024); https://doi.org/10.1117/12.3048544
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KEYWORDS
Matrices

Image enhancement

Deep learning

Image processing

Error analysis

Motion analysis

Detection and tracking algorithms

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