Comparison of kinematics and kinetics between OpenCap and a marker-based motion capture system in cycling

This study evaluates the agreement of marker-based and markerless (OpenCap) motion capture systems in assessing joint kinematics and kinetics during cycling. Markerless systems, such as OpenCap, offer the advantage of capturing natural movements without physical markers, making them more practical for real-world applications. However, the agreement of OpenCap with a marker-based system, particularly in cycling, remains underexplored. Ten participants cycled at varying speeds and resistances while motion data were recorded using both systems. Key metrics, including joint angles, moments, and joint reaction loads, were computed using OpenSim and compared using root mean squared error (RMSE) per trial across participants, Pearson correlation coefficients (r) per trial across participants and repeated measures Bland-Altman to control trials' dependency within subject. Results revealed very strong agreement (r > 0.9) for hip (flexion/extension), knee (flexion/extension), and ankle (dorsiflexion/plantarflexion) joint angles. Relatively high RMSE values of 10.7° (±3.0°) and 12.4° (±4.6°) were observed for left and right ankle dorsiflexion/plantarflexion angles, respectively, per trial across participants. Knee flexion/extension RMSE per trial across participants were 9.3° (±3.8°) and 10.2° (±4.3°) for the left and right limbs, respectively. RMSE values of hip flexion/extension, adduction/abduction and rotation were 7.9° (±2.6°), 3.2° (±1.1°) and 3° (±1.2°) for the left side. Joint reaction forces and moments exhibited moderate to very strong agreement across most degrees of freedom. The RMSE of joint reaction forces ranged from 13.7 to 37.7 %BW. The hip medial-lateral moment had a minimum RMSE of 0.19 %BW × ht, while the highest RMSE of 1.27 %BW × ht was in the knee anterior-posterior moment. Despite strong overall agreement between the systems, variability per trial across participants in RMSE suggested that OpenCap may require further refinement in specific areas. These findings highlight the potential of markerless motion capture systems, such as OpenCap, in biomechanical analyses of cycling while also identifying areas where further refinement may be needed.