基于Kinect的体育运动自训练系统
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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。
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.
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.
引文
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[2] 李青,王青.体感交互技术在教育中的应用现状述评[J].远程教育杂志,2015,33(1):48-56.
[3] YONG T H,TAN D Y W.Preliminary investigation of mo-vement-heart rate relationship using kinect-based badminton performance analysis[C]//International conference on robotics,automation and sciences.Melaka:[s.n.],2017:1-5.
[4] 杨张振.基于Kinect的摔倒行为研究[J].计算机技术与发展,2018,28(4):179-182.
[5] 张莹莹,郭星.基于Kinect动态手势识别算法的研究与实现[J].计算机技术与发展,2017,27(12):11-15.
[6] ISLAM M U,MAHMUD H,ASHRAF F B,et al.Yoga posture recognition by detecting human joint points in real time using microsoft Kinect[C]//2017 IEEE region 10 humanitarian technology conference.Dhaka:IEEE,2017:668-673.
[7] SIELUZYCKI C,KACZMARCZYK P,SOBECKI J,et al.Microsoft kinect as a tool to support training in professional sports:augmented reality application to Tachi-Waza techniques in Judo[C]//2016 third European network intelligence conference.Wroclaw:[s.n.],2016:153-158.
[8] ZHANG L,HSIEH J C,TING T T,et al.A kinect based golf swing score and grade system using GMM and SVM[C]//2012 5th international congress on image and signal processing.Chongqing,China:[s.n.],2012:711-715.
[9] PAYEUR P,NASCIMENTO G M G,BEACON J,et al.Human gesture quantification:an evaluation tool for somatic training and piano performance[C]//2014 IEEE international symposium on haptic,audio and visual environments and games.Richardson,TX:IEEE,2014:100-105.
[10] 吴齐云.基于Kinect的上肢运动康复交互系统研究[D].广州:广东工业大学,2016.
[11] 张利英.基于Kinect运动捕捉的太极拳辅助训练系统研究[D].石家庄:河北科技大学,2016.
[12] 穆道宏.引体向上的价值与练习步骤[J].中国学校体育,2008(5):52.
[13] 林海波,梅为林,张毅,等.基于Kinect骨骼信息的机械臂体感交互系统的设计与实现[J].计算机应用与软件,2013,30(2):157-160.
[14] 蓝箭,李印,赵炎,等.一种基于肢体形态的虚拟试衣方法研究[J].工业控制计算机,2016,29(1):105-106.
[15] 蓝箭,孙娟,杨裕炳,等.基于Kinect骨骼追踪的控制方法的研究[J].仪表技术,2014,43(8):20-22.
[16] 李茜,宋梦,文豪.基于kinect骨骼追踪技术的机器人运动系统的设计与实现[J].计算机与数字工程,2017,45(11):2187-2190.
[17] SATYAVOLU S,BRUDER G,WILLEMSEN P,et al.Analysis of IR-based virtual reality tracking using multiple Kinects[C]//2012 IEEE virtual reality workshops.Costa Mesa,CA:IEEE,2012:149-150.
[18] 战荫伟,于芝枝,蔡俊.基于Kinect角度测量的姿势识别算法[J].传感器与微系统,2014,33(7):129-132.
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