多维运动学特征与SVM融合的俯卧撑动作质量评估方法OA

To address the problems of low evaluation accuracy caused by subtle compensatory movements and a single scoring dimension in automated push-up assessment,this paper proposes a push-up movement quality assessment via multi-dimensional kinematic features and SVM.The method employs MediaPipe BlazePose to extract 2D coordinates of eight upper-body keypoints and compute the angle sequences of elbow joints and body.After Savitzky-Golay filtering and peak detection,the push-up video is segmented into action cycles.Within each cycle,five kinematic features are computed:pushing depth,upper-limb symmetry,trunk posture,trunk symmetry,and core stability.An SVM with RBF kernel is used to perform binary classification of actions as compliant or abnormal.For compliant actions,the multi-dimensional FastDTW algorithm is applied to align with a standard template and output a quality score on a 100-point scale.Experiments on the Kaggle public push-up dataset demonstrate that the proposed method simultaneously achieves action counting,quality assessment,and 100-point scoring,with an inference speed of less than 0.1s per video,showing its potential for rapid video-based action evaluation.