Processed Data

The biomechanical analysis of human movement, particularly gait, is crucial in fields such as clinical medicine, sports, and rehabilitation. While traditional motion capture (Mocap) systems are effective, they are often limited by their complexity, high cost, and the unnatural settings they require in terms of the gesture and motion environment. Emerging tools like inertial sensors and mark- erless video-based systems offer greater flexibility but encounter challenges in motion cycle segmentation, as they present kinematic data as time series, adding new difficulties to the analysis. This paper introduces a novel machine learning-based system for automatic gait cycle segmentation using features extracted from two easily measurable lower limb kinematic variables: hip and knee extension angles. The proposed method leverages instantaneous information from these angles for segmentation, ensuring versatility and independence from specific data collection methods. This allows for rapid segmentation and potential implemen-tation on lower-performance processors. Experimental results demonstrate the high accuracy and efficiency of the proposed algorithm segmenting the gait cycle.The F1-score was 0.997. By using readily available hip and knee kinematic data and identifying crucial biomechanical relationships, our method offers a versa-tile and practical solution for motion analysis across various clinical and sports applications.

人体运动生物力学分析，尤其是步态分析，在临床医学、体育与康复等领域至关重要。传统动作捕捉（Motion Capture, Mocap）系统虽效果优异，但常受限于其复杂性、高昂成本，以及对肢体姿态与运动环境的非自然要求。诸如惯性传感器、无标记视频系统等新兴工具虽具备更高灵活性，但在运动周期分割任务中面临挑战：这类工具将运动学数据以时间序列形式呈现，为分析增添了新的难度。本文提出一种基于机器学习的新型步态周期自动分割系统，其依托从两项易于测量的下肢运动学变量——髋部伸展角与膝部伸展角——中提取的特征实现分割。所提方法利用上述角度的瞬时信息完成分割，兼具通用性且无需依赖特定的数据采集方式，可实现快速分割，且能够在低性能处理器上部署。实验结果表明，所提步态周期分割算法具备高精度与高效率，其F1分数达到0.997。本方法依托易获取的髋部与膝部运动学数据，并挖掘关键生物力学关联，可为各类临床与体育场景下的运动分析提供通用且实用的解决方案。