The Concurrent Validity and Reliability of an In-Lab Markerless Motion Capture System in Assessing Joint Kinematics of the Competitive Tennis Player: A Pilot Study

Marker-based (MB) motion capture systems are considered the gold standard in biomechanics, but markerless (ML) systems, such as Theia3D, offer potential for more accessible and time-efficient data collection. However, the validity and reliability of ML systems in accurately capturing joint angular velocities in dynamic movements, such as tennis serves, is not well established. The PURPOSE of this pilot study was to evaluate the concurrent validity and reliability of an in-lab ML system in measuring joint angular velocities in competitive tennis players, compared to an MB system. METHODS: Nine collegiate tennis players performed serves while both MB and ML systems recorded their joint kinematics. Angular velocities for the left knee, right knee, pelvis, and trunk were analyzed. Pearson correlation coefficients, Concordance Correlation Coefficients (CCC), and Bland-Altman plots were used to assess precision, accuracy, and bias between the two systems. RESULTS: The ML system demonstrated strong agreement with the MB system across all joints. For the right knee, Pearson correlation was high (r = 0.980), with excellent CCC values (CCC = 0.977) indicating high precision and accuracy, and Bland-Altman plots revealed minimal bias. The left knee also showed good agreement (r = 0.812, CCC = 0.779), with a slight bias but strong overall precision. The pelvis angular velocity displayed good agreement (r = 0.866, CCC = 0.816), while the trunk angular velocity (r = 0.885, CCC = 0.729) demonstrated strong accuracy and precision, with Bland-Altman plots showing limited bias for both. CONCLUSION: The ML system showed promising results in assessing joint angular velocities in tennis players, with strong precision and accuracy as demonstrated by the Pearson correlations, CCC, and Bland-Altman analyses. These findings suggest that the ML system could be a reliable alternative to MB systems for capturing lower body and trunk kinematics, with minimal systematic bias observed in key joint angular velocities. Future studies should further investigate its applicability across a broader range of movements and populations.