# EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
## 4 Discussion
### EMG and Muscle Forces  
The difference between adjusted and experimental EMGs were comparable to the differences between the EMG envelopes obtained from two repetitions of the 6 kg trials (See Appendix B). Accordingly, the adjusted EMGs were considered representative of the experimental EMGs. The muscles that exhibited the largest differences were the posterior deltoid, pectoralis major, latissimus dorsi, and triceps. In the second half of the lift, the triceps was mostly involved in the elbow flexion, thus the increased length of the MTU was balanced by a reduction of its EMG, to reduce its contribution to the net moments of the GH joint. Pectoralis major and latissimus dorsi generated torques at the GH, sternoclavicular and acromioclavicular joints. Since the calibration was performed by matching only GH joint torque, the error in calibration for these muscles was probably larger. Similar results have been reported for the lower limb. The activations predicted with a multi-joint (hip, knee, ankle) model were more accurate than using a single joint model [16]. Overall, the agreement of the EMGs offered a preliminary validation of the activations predicted with EMGA.  
As shown in previous studies on the upper limb, lower limb, or the spine, activation patterns predicted by EMG-informed algorithms differ from SO based predictions [15; 41]. SO, based on least squares activation, tends to favor muscles with large moment arms [42] and underestimates co-contraction [43]. While this could be easily observed for simple humeral motions such as abduction or flexion, it was less straightforward for our lifting task due to its kinematic complexity. Overall, SO reduced activation for most muscles, in comparison to activations predicted by the EMGA method. This tendency was even more evident on the overhead phase, where the supraspinatus and subscapularis activations were minimized with SO and SOcal, even though such positions are known to be at higher risk for injury, and thus would require more activation of the rotator cuff muscles [44; 45; 46]. Similarly, during the concentric phase of the task (0%-30%), teres major was expected to assist the latissimus dorsi in the extension of the humerus [47], which was not predicted when using SO orSOcal. Accordingly, the difference in the empiric activation distribution was probably due to the lack of a co-contraction component in SO and SOcal. This limitation of SO could also be inferred from the random correlations between the rotator cuff muscles that were not consistent between trials. Whereas, the activations predicted by the EMGA method showcased correlations between the rotator cuff muscles irrespective of the task and load, as well as between agonist and antagonist muscles. Additionally, the observed loop-shape co-activation was reported experimentally as a typical force sharing pattern in cat walking [48]. Muscle activations were obtained without the implementation of a non-dislocation constraint, which reduced the effect of mathematical penalties and restrictions that might not be physiological. Indeed, different muscle recruitment trends could be predicted depending on the implementation of a stability constraint [20; 26]. The implications of such a constraint could be detrimental when studying unstable shoulders, that might physiologically fail to ensure stability, but would succeed to do so in a constrained model.