摘要
文章针对高校学生体质测试训练的需求,以立定跳远和引体向上项目为研究重点,提出了一种基于计算机视觉的智能体测辅助训练方案。通过分析训练者不同动作的关键点,自动捕捉动作的轨迹与姿态变化,实现动作规范性的智能评估,从而帮助受训者全面了解动作表现并加以改进,提升训练质量和测试成绩。该方案为高校体质测试训练的智能化与标准化提供了技术支持和创新思路。
Aiming at the needs of college students’physical fitness test training,the paper proposes an intelligent physical test assisted training program based on computer vision,with the standing long jump and pull-up program as the research focus.By analyzing the key points of different movements of the trainer,it automatically captures the trajectory and posture changes of the movements and realizes the intelligent assessment of movement normality.Thus,it helps the trainees to fully understand the movement performance and improve it,which in turn improves the training quality and test scores.It provides technical support and innovative ideas for the intelligence and standardization of college physical fitness test training.
作者
郭才
洪英汉
梁思睿
林培基
GUO Cai;HONG Yinghan;LIANG Sirui;LIN Peiji(Hanshan Normal University,Chaozhou Guangdong 521041,China;Guangzhou Maritime University,Guangzhou Guangdong 510725,China)
出处
《信息与电脑》
2025年第3期161-163,共3页
Information & Computer
基金
2023年韩山师范学院博士启动项目“基于深度学习的运动模糊图像复原算法的研究与实现”(项目编号:QD202324)。
关键词
体质测试
辅助训练
姿态识别
physical test
auxiliary training
pose recognition
作者简介
郭才,男,博士,高级工程师。研究方向:深度学习、计算机视觉;通信作者:洪英汉,男,博士,正高级工程师。研究方向:数字工程应用。
