[1]李鑫,陈建新,陈克坚,等.基于Kinect的体育运动自训练系统[J].计算机技术与发展,2019,29(04):122-127.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 025]
　LI Xin,CHEN Jian-xin,CHEN Ke-jian,et al.Kinect-based Sports Self-training System[J].,2019,29(04):122-127.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 025]
点击复制

基于Kinect的体育运动自训练系统()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年04期
页码:
122-127
栏目:
应用开发研究
出版日期:
2019-04-10

文章信息/Info

Title:
Kinect-based Sports Self-training System
文章编号:
1673-629X(2019)04-0122-06
作者:
李鑫1; 陈建新1; 陈克坚2; 周旭东2
1. 南京邮电大学 通信与信息工程学院,江苏 南京 210000;2. 南京邮电大学 电子与光学工程学院、微电子学院,江苏 南京 210000
Author(s):
LI Xin1; CHEN Jian-xin1; CHEN Ke-jian2; ZHOU Xu-dong2
1. School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210000,China;2. School of Electronic and Optical Engineering and School of Microelectronics,Nanjing University of Posts and Telecommunicatio
关键词:
Kinect; 骨骼图像; 深度图像; 引体向上; 打分系统
Keywords:
Kinect; skeleton image; depth image; pull-ups; rating system
分类号:
TP391. 42
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 04. 025
摘要:
学生体质关系着民族未来发展,而体质测试是衡量学生体质的主要手段。传统测试主要通过教师来实施,从而增加了教师的工作量,同时也可能导致测试标准不统一。这不仅为师资缺乏的地区增加了难度,还增加了体质测试的不公平性,因而研究自主测试系统具有重要意义。利用微软公司推出的深度传感器,对体育项目进行自动测试,并达到实时测量体育运动的效果,应用于学生体育项目引体向上。根据深度传感器信息确定横杆位置,并利用骨骼跟踪确定测试者下颌位置,通过手臂的三个关节点确定手臂弯曲度;利用下颌到横杆的距离和手臂的伸直程度对本次动作进行评分和计数。同时使用者可以通过动作视频回放和评分情况进行自我调整,达到更好的训练效果。
Abstract:
Student health is related to the development of nation,and the measurement of health is the main approach to evaluate the health of students. The traditional method is performed by the teachers,which adds extra workload,and may also lead to inconsistent test standards. This not only increases the difficulty of areas with lack of teachers,but also increases the unfairness of physical fitness testing. Therefore,it is necessary to study the self-measurement system. We use the deep sensor developed by Microsoft to evaluate the sports training such as the pull-up. The position of the horizontal bar is determined according to the depth information from Kinect,and the bending angle of the arm is determined according to three joints in the arm. The distance of the mandible to the horizontal bar and the degree of extension of the arm are used to evaluate the movement and score. At the same time,users can self-assess and exercise through the video playback and scoring,which can improve the training effect.

相似文献/References:

[1]黄露丹,严利民.基于Kinect深度数据的人物检测[J].计算机技术与发展,2013,(04):119.
　HUANG Lu-dan,YAN Li-min.Human Detection Based on Depth Data Acquired by Kinect[J].,2013,(04):119.
[2]谢亮,廖宏建,杨玉宝.基于Kinect的姿势识别与应用研究[J].计算机技术与发展,2013,(05):258.
　XIE Liang,LIAO Hong-jian,YANG Yu-bao.Recognition and Application Research of Kinect-based Gesture[J].,2013,(04):258.
[3]苏 卓,喻春阳.基于2D 图像变换的虚拟试衣算法[J].计算机技术与发展,2018,28(02):24.[doi:10．3969/j．issn．1673－629X．2018．02．006]
　SU Zhuo,YU Chunyang.A Virtual Fitting Algorithm Based on 2D Image Transformation[J].,2018,28(04):24.[doi:10．3969/j．issn．1673－629X．2018．02．006]
[4]杨雷坤[] [],李绪志[],王红飞[]. 基于手势识别技术的遥操作系统的设计与实现[J].计算机技术与发展,2015,25(09):1.
　YANG Lei-kun[] [],LI Xu-zhi[],WANG Hong-fei[]. Design and Implementation of Tele-operations System Based on Gesture Recognition Technology[J].,2015,25(04):1.
[5]王劲东,武频. 一种基于Kinect的指尖检测算法[J].计算机技术与发展,2016,26(07):14.
　WANG Jin-dong,WU Pin. An Algorithm of Fingertip Detection Based on Kinect[J].,2016,26(04):14.
[6]罗章[][][],谭海波[][],李晓风[][][],等. Kinect运动捕获技术在健康医疗中的应用研究[J].计算机技术与发展,2016,26(08):104.
　LUO Zhang[][][],TAN Hai-bo[][],LI Xiao-feng[][][],et al. Research on Application of Kinect Motion Capture Technology in Health Care[J].,2016,26(04):104.
[7]耿曼[][],钱国明[],王宇[]. 虚拟试衣系统服装图像匹配算法研究[J].计算机技术与发展,2017,27(01):126.
　GENG Man[] [],QIAN Guo-ming[],WANG Yu[]. Research on Clothing Image Matching of Virtual Fitting Room[J].,2017,27(04):126.
[8]陈国军,孔李燕,张清伟,等.基于RGB-D三维点云目标分割[J].计算机技术与发展,2018,28(12):38.[doi:10．3969/j． issn．1673－629X．2018．12．008]
　CHEN Guojun,KONG Liyan,ZHANG Qingwei,et al.3D Point Cloud Target Segmentation Based on RGB－D Data[J].,2018,28(04):38.[doi:10．3969/j． issn．1673－629X．2018．12．008]
[9]陈国军,张清伟,李开悦,等.基于RGB—D树状结构物体三维重建[J].计算机技术与发展,2018,28(12):142.[doi:10．3969／j．issn．1673—629X．2018．12．030]
　CHENGuo-jan,ZHANG Qing-wei,LI Kaiyue,et al.3D Reconstruction of Tree Structure Based on RGB—D[J].,2018,28(04):142.[doi:10．3969／j．issn．1673—629X．2018．12．030]
[10]梁正友,黄思捷,孙 宇,等.基于区域分割的多视角点云精简算法[J].计算机技术与发展,2021,31(06):40.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 008]
　LIANG Zheng-you,HUANG Si-jie,SUN Yu,et al.Multi-view Point Cloud Reduction Algorithm Based onRegion Segmentation[J].,2021,31(04):40.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 008]
[11]杨张振.基于Kinect 的摔倒行为研究[J].计算机技术与发展,2018,28(04):179.[doi:10.3969/ j. issn.1673-629X.2018.04.038]
　YANG Zhang-zhen.Research on Fall Detection Based on Kinect[J].,2018,28(04):179.[doi:10.3969/ j. issn.1673-629X.2018.04.038]

更新日期/Last Update: 2019-04-10