In human movement analysis, multibody models are an indispensable part of the process both for marker-based and video-based markerless approaches. Constituents (segments, joint constraints, body segment inertial parameters etc.) of such models and choice of the modelers play an important role in the accuracy of estimated results (segmental and joint kinematics, segmental and whole-body center of mass positions etc.). For marker-based method, although standard models exist, particularly for the lower extremity (e.g., Conventional Gait Model or models embedded in OpenSim), there seems to be a lack of consolidated explanation on the constituents of the whole-body model. For the markerless approach, multibody kinematic models (e.g., the Theia3D model) have been in use lately. However, there is no clear explanation on the estimated quantities (e.g., joint centers, body surface landmarks etc.) and their relation to the underlying anatomy. This also motivates the need for a description of the markerless multibody model. Moreover, comparing markerless results to those of classical marker-based method is currently the most commonly used approach for evaluation of markerless approaches. This study first aims to develop and describe a whole-body marker-based model ready to be used for human movement analysis. Second, the markerless multibody model embedded in Theia3D is described and inertial parameters are redefined. We also report assessment of the markerless approach compared to marker-based method for a static T-pose performed by 15 subjects. Finally, we disseminate the marker-based and markerless multibody models for their use in Visual3D.

Background Balance studies usually focus on quantities describing the global body motion, such as the position of the whole-body centre of mass (CoM), its associated extrapolated centre of mass (XCoM) and the whole-body angular momentum (WBAM). Assessing such quantities using classical marker-based approach can be tedious and modify the participant's behaviour. The recent development of markerless motion capture methods could bypass the issues related to the use of markers. Research question Can we use markerless motion capture systems to study quantities that are relevant for balance studies? Methods Sixteen young healthy participants performed four different motor tasks: walking at self-selected speed, balance loss, walking on a narrow beam and countermovement jumps. Their movements were recorded simultaneously by marker-based and markerless motion capture systems. Videos were processed using a commercial markerless pose estimation software, Theia3D. The position of their CoM was computed, and the associated XCoM and WBAM were derived. Bland-Altman analysis was performed and root mean square error and coefficient of determination were computed to compare the results obtained with marker-based and markerless methods across all participants and tasks. Results Bias remained of the magnitude of a few mm for CoM and XCoM position, and RMSE of CoM and XCoM was around 1 cm. Confidence interval for CoM and XCoM was under 2 cm except for one task in one direction. RMSE of the WBAM was less than 8% of the total amplitude in any direction, and bias was less than 1%. Significance Results suggest that the markerless motion capture system can be used in balance studies as the measured errors are in the range of the differences found between different models or populations in the literature. Nevertheless, one should be careful when assessing dynamic movements such as jumping, as they displayed the biggest errors.