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基于视觉感知-运动识别算法的学生体育锻炼行为分析与健康教育策略

Analysis of Students' Sports Exercise Behavior and Health Education Strategy Using Visual Perception-Motion Recognition Algorithm.

作者信息

Chen Minwei, Zhou Yunzheng

机构信息

College of Physical Education, Chongqing University, Chongqing, China.

College of Physical Education and Physical Medicine, Chongqing Medical University, Chongqing, China.

出版信息

Front Psychol. 2022 May 13;13:829432. doi: 10.3389/fpsyg.2022.829432. eCollection 2022.

DOI:10.3389/fpsyg.2022.829432
PMID:35645860
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9136329/
Abstract

This study aims to explore the future development path of the college health education and health education's impact on students' sports exercise. Specifically, artificial intelligence (AI) algorithm is combined with intelligent robotics technology to acquire and analyze students' sports exercise behaviors. As a result, a new development model is formulated for college health education. First, it explores students' sports exercise and health education situation in Chinese higher institutions and uncovers the underlying problems. Then it puts forward the corresponding modification suggestions. Second, the AI algorithm and the Kinect sensor-mounted intelligent robot capture the human skeleton features to obtain smooth skeleton joint points data. At the same time, a visual perception human motion recognition (HMR) algorithm is established based on the Hidden Markov Model (HMM). Afterward, the proposed HMM-based HMR algorithm is used to recognize students' sports exercise motions by analyzing human motion skeleton images. The experimental outcomes suggest that the maximum reconstruction error of the HMR algorithm is 10 mm, and the compression ratio is between 5 and 10; the HMR rate is more than 96%. Compared with similar algorithms, the proposed visual perception HMR algorithm depends less on the number of training samples. It can achieve a high recognition rate given only a relatively few samples. Therefore, the proposed (AI + intelligent robot)-enabled HMM-based HMR algorithm can effectively identify the behavior characteristics of students in sports exercise. This study can provide a reference for exploring college students' health education development path.

摘要

本研究旨在探索高校健康教育的未来发展路径以及健康教育对学生体育锻炼的影响。具体而言，将人工智能（AI）算法与智能机器人技术相结合，以获取和分析学生的体育锻炼行为。由此，为高校健康教育制定了一种新的发展模式。首先，探究中国高校学生的体育锻炼和健康教育情况，揭示潜在问题，然后提出相应的改进建议。其次，AI算法与安装了Kinect传感器的智能机器人捕捉人体骨骼特征，以获取平滑的骨骼关节点数据。同时，基于隐马尔可夫模型（HMM）建立视觉感知人体运动识别（HMR）算法。之后，利用所提出的基于HMM的HMR算法，通过分析人体运动骨骼图像来识别学生的体育锻炼动作。实验结果表明，HMR算法的最大重建误差为10毫米，压缩比在5到10之间；HMR率超过96%。与类似算法相比，所提出的视觉感知HMR算法对训练样本数量的依赖性较小。仅需相对较少的样本就能实现较高的识别率。因此，所提出的基于（AI+智能机器人）的HMM的HMR算法能够有效识别学生体育锻炼中的行为特征。本研究可为探索大学生健康教育发展路径提供参考。

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