Real-time Motion Capture and Visual Feedback for Amputation Gait Training

People with transfemoral amputation face challenges such as gait asymmetry, instability, and increased energy consumption due to loss of the knee joint. Because of the loss of proprioceptive feedback from their missing limbs, visual feedback is essential for gait correction. Additionally, current visual feedback systems lack portability, cost-effectiveness, and they fail to provide precise, intuitive feedback on spatiotemporal parameters, joint angles, and both frontal and sagittal plane information, limiting their effectiveness in correcting gait abnormalities.This study aims to investigate whether gait training using a real-time 2D motion capture and visual feedback gait training system (2DMV) can improve the gait biomechanics and psychosocial functions of people with unilateral transfemoral amputation (uTFA). The 2DMV system analyzes spatiotemporal parameters and joint kinematics during gait and displays the uTFA's image on a screen, providing visual feedback specifically targeting gait abnormalities. This allows individuals with uTFA to intuitively understand the feedback and make real-time gait adjustments. The biomechanical parameters include joint kinematics, gait symmetry, and walking performance.