A Empirical Research on AI-Powered Athletic Posture Detection in Sports Training

Author:

Wang S.,Zhang G.

Abstract

Abstract: The current investigation delineates the efficacy of AI-facilitated detection of athletic postures within the realm of sports training. Employing a synthesis of literature review and empirical methodologies, data were amassed and scrutinized, affirming the study’s validity. The salient outcomes are manifold: (1) The frame difference algorithm efficaciously discerns inter-frame variances, evidencing pronounced adaptability and robustness, thereby enabling the recognition of weightlifting postures. (2) Confronting the challenge of negligible inter-frame disparities inherent in the frame difference algorithm, the research introduces a novel detection technique predicated on the cumulative inter-frame differences, which precisely pinpoints regions of posture alteration in weightlifting athletes. (3) Leveraging the dynamic space model of optical flow, the study ascertains the directional channel predicated on optical flow trajectory analyses, facilitating the identification of three distinct weightlifting postures: squatting, descending, and standing. (4) In alignment with the distinctive postural attributes of weightlifting athletes, a human posture paradigm was formulated, and a BP neural network classifier was deployed for both training and evaluative purposes, culminating in the successful differentiation of athlete from non-athlete entities within the training milieu. (5) The application of AI in posture recognition was extended to the scrutiny of pivotal postures and motions in weightlifting athletes, with experimental findings revealing a 98.21% accuracy rate in the recognition of force-exertion postures via the inter-frame difference method, and a flawless 100% accuracy in the identification of the apex and squatting postures. The enumeration of detected postures—encompassing knee extension, knee flexion, force application, squatting, and standing—through the poselet keyframe extraction approach, corresponded with the video count. Prospectively, AI’s role in athletic posture detection promises to augment coaches’ and athletes’ comprehension of their proficiencies and deficiencies, thereby steering training refinement and bolstering both the efficacy of training and the athletes’ caliber.

Publisher

Scipedia, S.L.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3