Quantifying gait compensation in knee osteoarthritis using smartphone-based motion capture (OpenCap)

Knee osteoarthritis (KOA) is one of the most prevalent degenerative joint diseases, and its pathological features often lead to abnormal gait patterns and limited joint mobility. These changes induce various degrees of lower-limb compensatory mechanisms that significantly affect patients' quality of life and increase their risk of falls. An accurate and objective assessment of these gait changes and compensatory strategies is critical for clinical diagnosis, monitoring of disease progression, and the formulation of rehabilitation strategies.

This study aims to investigate the compensatory mechanisms of lower limb kinematics in KOA patients during walking using a markerless motion capture system-OpenCap-and toevaluate its feasibility and accuracy in a clinical environment.

A total of 33 KOA patients and 78 healthy control participants were enrolled. Two smartphones were used to record videos of participants walking along a flat path, and OpenCap was employed to calculate spatiotemporal gait parameters and 3D joint kinematics. Data were statistically analyzed to determine differences in gait features between KOA patients and controls.

KOA patients had significantly reduced walking speed and stride length, and exhibited increased step width, reduced knee flexion-extension range, and greater pelvic tilt and hip internal rotation during certain phases of the gait cycle. These findings reflect biomechanical compensation strategies related to joint pain, instability, or restricted mobility. OpenCap provided reliable and accurate motion data and demonstrated strong potential for hospital-based gait assessments due to its low cost and ease of setup.

This study demonstrated that OpenCap effectively captures KOA-related gait abnormalities and compensatory joint movements. Its low cost and ease of use support its application in hospital settings for dynamic evaluation and rehabilitation planning.