Many are affected by musculoskeletal disorders, for example rotator cuff tears or knee osteoarthritis. Disorders affect muscle coordination, which can negatively impact treatment of these disorders. In order to offer personalized treatment options, muscle recruitment needs to be predicted accurately in these patients. Musculoskeletal modeling can be used to estimate muscle coordination non-invasively, but established methods like Static Optimization (SO) underestimate co-contraction of muscles. Predicting co-contraction is important, because it is associated with musculoskeletal disorders through joint stability, joint stiffness and compensatory muscle control strategies, e.g. to minimize pain. State-of-the-art methods used to predict co-contraction include assistance from electromyography (EMG) measurements or joint stability constraints. The existing Rapid Muscle Redundancy (RMR) solver utilizes a glenohumeral stability constraint to predict co-contraction in the deep rotator cuff muscles. The addition of EMG-assistance to this solver, might improve it's capability to predict co-contraction in superficial muscles as well.

Therefore, the main goal of this thesis is to equip the existing RMR solver with an EMG-assisted cost function. Along with this extension, the RMR solver is revised to be class-based in order to improve modularity, scalability and user interaction. The new implementation is verified and is able to utilize EMG-tracking alongside the glenohumeral joint stability constraint.

Because EMG is difficult to normalize reliably and is less practical to acquire outside lab environments, this study focuses on evaluating the effect of providing one EMG signal at the time. Thus, the second goal involves investigating the effect of tracking single EMG signals on muscle activation using as shoulder model and data. It is revealed that the impact of EMG-tracking single EMG signals on muscle recruitment is minimal, with Mean Absolute Error (MAE) and Zero-normalized Cross-Correlation (ZNCC) changes that are generally less than 0.005 and 0.03, respectively.

Since the role of muscle coordination on joint loading is crucial in pathologies like knee osteoarthritis, the third goal is to evaluate the effect of EMG-tracking on knee joint contact force (JCF). To this end estimations are performed on gait data, which includes in-vivo knee implant reaction forces.
EMG-tracking tasks generally cause higher knee JCF during stance. Tracking the most anterior element of the Gluteus Medius improves knee JCF accuracy, with respect to ground truth in-vivo data, the most on average over three subjects and multiple trials, with the Root Mean Squared Error (RMSE) decreasing with 8% and the Zero-Normalized Cross-Correlation (ZNCC) increasing with 4%, although unreliable EMG-normalization raises questions regarding the legitimacy of these results.
The potential of EMG-tracking is especially demonstrated with tracking the Rectus Femoris muscle in challenge 6, in which more co-contractionis present. Through an increase of the the Rectus Femoris activity as well as its antagonist, Semitendinosus, the knee load estimation is increased at the first peak during stance, which was previously underestimated without EMG-assistance.
However, compared to results from SO and CEINMS, our EMG-assisted RMR solver only achieves lower RMSE in 2 out of 14 trials, although the average difference is only 0.03 body weight.

While the new implementation has not proved itself definitively, it seems promising in predicting co-contraction using EMG-assistance from few EMG signals. In the future this or similar methods might play a crucial role in investigating changes in muscle coordination due to rotator cuff tears, knee osteoarthritis or other musculoskeletal pathologies. This can contribute to more personalized prevention, intervention or rehabilitation.