久坐人群无器械训练动作识别与计数算法研究  

Research on the recognition and counting algorithm of sedentary people′s training without equipment

作　　者：王威 孙少明[1,3] 孙怡宁[1] 陈超 陈竟成[1,2] 张海涛[1] Wang Wei;Sun Shaoming;Sun Yining;Chen Chao;Chen Jingcheng;Zhang Haitao(Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China;University of Science and Technology of China,Hefei 230026,China;CAS(Hefei)Institute of Technology Innovation,Hefei 230088,China)

机构地区：[1]中国科学院合肥物质科学研究院,合肥230031 [2]中国科学技术大学,合肥230026 [3]中科院合肥技术创新工程院,合肥230088

出　　处：《电子测量技术》2021年第2期109-114,共6页Electronic Measurement Technology

基　　金：中国科学技术大学智慧城市研究院(芜湖)科技成果转化项目(2019ZX01);国家重点研发计划(2018YFC2001304)项目资助。

摘　　要：针对久坐人群长期缺乏运动导致身体呈现亚健康状态和现有训练方法缺乏监督性的现状,提出一种人体动作识别与计数方法实现4种无器械训练动作精准识别与计数。以手机摄像头捕获训练者的视频信息作为输入,通过BlazePose网络模型处理得到的人体骨骼点数据经过数据滤波处理、特征提取后,利用3种常见的机器学习算法进行动作分类,将分类的结果结合骨骼信息,采用检测波峰波谷计数算法统计训练动作的完成次数。实验结果表明,采用GBDT算法得出动作识别率为96.5%,计数算法准确率为98.9%,具有良好的实际应用价值。In view of the current situation that sedentary people lack of exercise for a long time, resulting in sub-health state and the lack of supervision of existing training methods, this paper proposes a method of human motion recognition and counting to realize the accurate recognition and counting of four kinds of training without equipment. Taking the video information of the trainer captured by the mobile camera as the input, the human skeleton point data processed by the BlazePose network model is processed by data filtering and feature extraction, and three common machine learning algorithms are used for action classification. The classification results are combined with the bone information, and the peak and trough counting algorithm is used to count the number of training actions completed. The experimental results show that: using GBDT classification algorithm, the action recognition rate is 96.5%, and the accuracy of counting algorithm is 98.9%, which has good practical application value.

关 键 词：久坐人群 动作识别 骨骼数据 动作计数 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]