Optics Data

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.