自动化与信息工程2026，Vol.47Issue(4)：49-54,6.DOI:10.12475/aie.20260407
多维运动学特征与SVM融合的俯卧撑动作质量评估方法
Push-up Movement Quality Assessment via Multi-dimensional Kinematic Features and SVM
摘要
Abstract
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.关键词
俯卧撑/动作质量评估/运动学特征/支持向量机/人体姿态估计Key words
push-up/movement quality assessment/kinematic features/support vector machine(SVM)/human pose estimation分类
信息技术与安全科学引用本文复制引用
叶嘉庆,苟兴悦,张启仁,陈俊屹,郑斯敏,曹东..多维运动学特征与SVM融合的俯卧撑动作质量评估方法[J].自动化与信息工程,2026,47(4):49-54,6.基金项目
广东省研究生教育创新计划(2024JGXM_042) （2024JGXM_042）
广东省高等教育教学改革项目(粤教高函[2024]30号 （）
粤教高函[2026]4号). （）
