Musculoskeletal modeling has the opportunity to improve the rehabilitation process after a shoulder injury by monitoring the muscle activations and tendon strain during rehabilitation exercises. The PTbot project combines a robot arm and a Thoracoscapular Shoulder Model (TSM) in order to track the shoulder movement and give a physiotherapist insight on the what happens in a human body, but the performance of the TSM is not yet analyzed. This paper aims to get a better understanding of the TSM by testing its performance in a wide range of motion.

With an existing isometric dataset, the TSM is positioned in five different shoulder configurations. First, the model is scaled in four steps. With the help of the muscle moment arms an normalized fiber length of this scaled model, improvements have been made to the latissimus dorsi. This improved model is scaled a second time whereafter an RMR solver is run. This RMR solver computed the muscle activation levels and joint torque residuals of the TSM. The RMR solver is first run with experimental external forces applied on the hand, in order to validate the TSM by comparing the muscle activations to EMG data. After that, the RMR solver is run with artificial external forces, in order to understand which muscles are activated during push and pull exercises.

The Mean Average Error (MAE) between the muscle activation levels and experimental EMG measurements has been calculated for the validation of the TSM. They show that the TSM performs well when the shoulder planar elevation and shoulder elevation angles, but the performance decreases when these angles increase. The active fiber forces have been calculated and the muscles with the major contribution have been identified for pulling and pushing. The Infraspinatus, rapezius and deltoid play a major role for pushing exercises and the subscapularis, teres major and biceps for pulling exercises. The total muscle force needed for pushing is higher than for pulling.