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Comparison of Joint Kinematics and Spatiotemporal Gait Parameters Between Markerless and Marker-Based Motion Capture
Junqing Wang1,2, Qiang Zhang1, Biao Wang2
1Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, 37# Wainan Guoxue Road, Chengdu, Sichuan, China.
Purpose:
Theia3D, a markerless motion capture system, offers a practical alternative to marker-based systems for movement assessment. However, its concurrent validity in patients with knee osteoarthritis remains unevaluated. This study aimed to evaluate the concurrent validity of Theia3D against a marker-based system in measuring joint kinematics and spatiotemporal gait parameters in patients with knee osteoarthritis and to compare error metrics between patients and healthy controls.
Methods:
A total of 162 patients with advanced knee osteoarthritis and 50 healthy controls performed self-selected speed walking and sit-to-stand tasks. Data were simultaneously collected using markerless and marker-based systems. Measurement agreement and errors for spatiotemporal and kinematic parameters were assessed using Bland-Altman analysis, mean difference, Pearson correlation, intraclass correlation coefficient (ICC), and root mean square error (RMSE). Group differences in ICC and RMSE were evaluated using independent-samples t tests or Wilcoxon rank-sum tests.
Results:
Theia3D showed excellent agreement and very strong correlation with the marker-based system for spatiotemporal gait parameters. Sagittal hip and knee angles demonstrated good to excellent agreement, while agreement in most frontal and transverse joint angles was poor. Compared to controls, knee osteoarthritis patients showed significantly lower ICCs and higher RMSEs for certain joint angles.
Conclusion:
These findings suggest that Theia3D has potential as an alternative to marker-based systems for assessing spatiotemporal gait parameters and sagittal knee angles during walking. However, since most joint angle errors exceed the 5° clinical acceptability threshold, future work should expand training datasets to include diverse clinical populations and refine algorithms to enable broader clinical implementation.
