Development of a Lower Limb Gait Asymmetry Index with Cyclogram Analysis Using Image Processing

Ahstract- This study focuses on developing a gait asymmetry index and applying image processing techniques to cyclogram analysis. We evaluated gait asymmetry under different walking conditions, specifically examining the effects of wearing a knee brace and holding a weight in hand. Using inertial measurement units (IMUs), we collected gait data to analyze knee, hip, and ankle angles in the sagittal plane. The joint kinematics data were assessed using our proposed asymmetry index, which combines image processing techniques and linear regression models. The proposed asymmetry index incorporates cyclogram area, trend symmetry, and the longest length of the cyclogram shape, effectively capturing variations in symmetry and providing a comprehensive measure. The results demonstrate that the proposed asymmetry index can effectively distinguish between different gait conditions. The findings potentially offer insights for researchers and clinicians in human biomechanics, aiding in the planning of interventions and the assessment of motion patterns, as well as for developers of bipedal walking robots in the field of robotic locomotion, enhancing their ability to design hardware and evaluate motion patterns.