The Delft Hand and Wrist model is a recently created musculoskeletal model in the OpenSim environment. The current model lacks a validation of the thumb muscles. Therefore, the main goal of this work is to perform a quantitative trend validation by analyzing the correlation between experimentally measured muscle activity data and muscle forces estimated by the model from markerless motion capture data. The original model is reduced to a minimal model containing only the thumb muscles. A second model is created by changing optimal muscle fiber length, maximum isometric force and tendon slack length parameter values in the minimal model to values reported in literature to evaluate the effects of these adjustments on the estimated muscle force. An experiment is conducted in which participants are instructed to perform repetitive thumb motions while muscle activity and 2D kinematic data is captured. 3D kinematics are obtained through the machine learning toolboxes DeepLabCut and Anipose. Muscle forces are estimated through inverse dynamic static optimization in OpenSim. The results showed that the correlation between the estimated muscle forces and experimentally measured muscle activity data for both the minimal model and the adjusted model was very low to moderate, meaning both models yield unrealistic muscle force estimations. In contrast, the measured muscle activity and kinematic data show expected results thus are captured correctly, which is useful for reference in future work.
There is room for improvement of the Delft Hand and Wrist model. Nevertheless, this work provides suggestions for the optimization of the current model and paves the way towards muscle force estimation and quantitative validation using experimentally measured muscle activity data for the Delft Hand and Wrist model in OpenSim.