发表机构
School of Engineering Mathematics and Technology, University of Bristol（布里斯托大学工程数学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于BlazePose的无标记姿态估计框架，可从普通视频中提取抗阻训练动作的关节轨迹，实现深蹲、硬拉等动作的量化评估，为实验室外的便捷生物力学评估提供了可行方案。
AI 中文摘要
抗阻训练可能是一项高风险活动，安全的动作姿势对避免受伤至关重要。实验室环境下的动作分析可提供量化的技术评估，但难以普及。无标记姿态估计无需物理标记即可从图像或视频中推断人体关键点，有望成为技术评估的可行替代方案。本文提出一种基于普通视频片段评估抗阻训练技术的姿态估计框架，利用BlazePose从深蹲、卧推和硬拉视频中提取解剖学关键点，并转换为关节角度轨迹，其中以深蹲为主要研究案例。将轨迹与定义的标准重复动作进行比较，采用均方根误差（RMSE）评估。结果表明，该框架可恢复深蹲和硬拉的有意义运动模式，实现重复动作间的量化比较，并识别同一组动作内的技术差异。性能高度依赖于相机方向和视觉遮挡，非矢状面视图会扭曲二维关节角度估计。研究结果证明，无标记姿态估计可支持实验室环境外的便捷生物力学评估。
英文摘要
Resistance training can be a high risk activity, and safe form is essential to avoiding injury. Laboratory-based movement analysis provides quantitive technique assessment, yet is not easily accessible. Markerless pose estimation infers body landmarks from images or video without physical markers and could offer a feasible alternative for technique assessment. We present a pose estimation framework to evaluate resistance-training technique from ordinary video footage. Using BlazePose, anatomical landmarks were extracted from squat, bench press, and deadlift videos and converted into joint-angle trajectories, with the squat serving as the primary case study. Trajectories were assessed against a defined reference repetition using root mean square error (RMSE). Results show that the framework recovers meaningful kinematic patterns for the squat and deadlift, enabling quantitative comparison between repetitions and identification of technique variability within a set. Performance depended strongly on camera orientation and visual occlusion, with non-sagittal views distorting 2D joint-angle estimates. The findings demonstrate that markerless pose estimation can support accessible biomechanical assessment outside laboratory environments.
