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.