篇名 |
Using Machine-Learning Technology to Implement a Nonhardware and Inexpensive Posture Detection System for Analyzing Body Posture Angles in Front Crawl Swimming
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並列篇名 | Using Machine-Learning Technology to Implement a Nonhardware and Inexpensive Posture Detection System for Analyzing Body Posture Angles in Front Crawl Swimming |
作者 | Yu-Hung Hsu、Cheng-Hsiu Li |
英文摘要 | Swimming is a sport that relies heavily on motor skills. Inability to maintain adequate bodily balance in water prevents swimmers from remaining afloat and propelling themselves. Due to the difficulty of attaching reflective stickers or LED (light-emitting diode) emitters to the body while adjusting the swimming posture, it is not possible to capture the posture like during a bicycle fitting; the refraction of water also affects the detection of the body’s posture. Addressing the shortcomings, this study developed a low-cost nonhardware posture detection system based on machine-learning models in MediaPipe. The system provides real-time and post analyses of posture angles and posture lines during front crawl swimming, thereby facilitating observation of the relationship between angle at which the arm enters the water and the body horizon. Two participants practicing front crawl were invited to test the proposed system. The experimental results confirmed that the proposed system provides effective detection and analyses of posture lines and angles in swimmers. The study also proposed the algorithm for optimizing posture angle detection to solve the problems of posture line distortion and angle calculation errors that arise when MediaPipe was used to detect a human skeleton above a water line. The system does not require the installation of hardware and is inexpensive to deploy, and it can be widely applied in front crawl swimming lessons to help learners adjust their arm’s entry angle and body horizon to reduce forward drag and increase speed.
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起訖頁 | 131-143 |
關鍵詞 | machine learning、posture detecting system、body posture angles、front crawl swimming |
刊名 | 電腦學刊 |
期數 | 202402 (35:1期) |
DOI |
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