Gender Differences in Lower Body Biomechanics: Insights from High-Resolution Motion Capture for Computer Vision and Assistive Technologies

Abstract

This study systematically investigates gender differences in lower body biomechanics using high-resolution motion capture data from male and female participants. Analysis of the collected kinematic data revealed distinct biomechanical patterns driven by anatomical, neuromuscular, and movement strategy variations, which are evident in joint angle profiles, stride dynamics, and movement complexity during walking and related locomotor tasks. Understanding these differences is crucial for improving computer vision applications in rehabilitation and assistive robotics, where gender-specific baselines can enhance motion tracking accuracy, anomaly detection, and system calibration. The findings provide a data-driven foundation for developing intelligent motion analysis tools that optimize rehabilitation protocols, injury-risk assessment, and human-robot interaction for diverse populations.