基于人体骨架的非标准深蹲姿势检测方法

Detection method of non-standard deep squat posture based on human skeleton

针对健身者在健身过程中因缺乏监督指导而导致姿势不正确甚至危及健康的问题,提出了一种深蹲姿势实时检测的新方法。通过Kinect摄像头提取人体关节三维信息,对健身中最常见的深蹲行为进行抽象与建模,解决了计算机视觉技术对于细微动作变化难以检测的问题。首先,通过Kinect摄像头捕获深度图像,实时获取人体关节点的三维坐标;然后,将深蹲姿势抽象为躯干角度、髋部角度、膝部角度和踝部角度,并进行数字化建模,逐帧记录下角度变化;最后,在深蹲完成后,采用阈值比较的方法,计算一定时间段内非标准帧比率。如计算比率大于所给定阈值,则判定此次深蹲为不标准;如低于阈值则为标准深蹲姿势。通过对六种不同类型的深蹲姿势进行实验,结果表明,该方法可检测出不同类型的非标准深蹲姿势,并且在六种不同类型的深蹲姿势中平均识别率在90%以上,能够对健身者起到提醒指导的作用。

Concerning the problem that the posture is not correct and even endangers the health of body builder caused by the lack of supervision and guidance in the process of bodybuilding, a new method of real-time detection of deep squat posture was proposed. The most common deep squat behavior in bodybuilding was abstracted and modeled by three-dimensional information of human joints extracted through Kinect camera, solving the problem that computer vision technology is difficult to detect small movements. Firstly, Kinect camera was used to capture the depth images to obtain three-dimensional coordinates of human body joints in real time. Then, the deep squat posture was abstracted as torso angle, hip angle, knee angle and ankle angle, and the digital modeling was carried out to record the angle changes frame by frame. Finally, after completing the deep squat, a threshold comparison method was used to calculate the non-standard frame ratio in a certain period of time. If the calculated ratio was greater than the given threshold, the deep squat was judged as non-standard, otherwise judged as standard. The experiment results of six different types of deep squat show that the proposed method can detect different types of non-standard deep squat, and the average recognition rate is more than 90% of the six different types of deep squat, which can play a role in reminding and guiding bodybuilders.