# EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
## 4 Discussion
### Joint Reaction Forces  
The pattern of the GH joint reaction forces obtained from EMGA were similar to that reported in-vivo using an instrumented prosthesis [49; 50] or via modelling [7], with higher amplitudes close to the maximum elevation and a higher contribution of the compressive component of the force. Our study however reported a higher force magnitude (mean peak value: 2907 N vs. about 1000 N). This could partly be explained by the difference in the lifted weights (6 vs. 2 kg), the age and fit of the studied population, and the kinematic differences between the two studies [51]. Additionally, the use of an implant, for the in-vivo study might have shifted the GH center of rotation and muscles moment arms [50]. The GH force calculated by SO had random peaks throughout the trials. However, these peaks disappeared with the calibration (SOcal). They occurred when GH main actuators (i.e., deltoid and pectoralis major) had activation peaks. Since these main actuators have a large contribution to the net joint reaction force, their activation patterns would reflect on the GH joint reaction amplitude. The model calibration, as discussed in Section 1, enabled the MTUs to generate the inverse dynamics joint moment without saturating these main actuators, and thus eliminated these peaks from the GH joint reaction force with SOcal.  
The muscle activations obtained from SO led to an unstable glenohumeral joint, as evidenced by the excursions outside the non-dislocation stability cone. The GH stability is probably attained, in part, by the co-contraction of rotator cuff muscles [52]. However, these muscles tend to have a smaller moment generating capacity. Thus, SO solutions would not favor their activation in comparison to that of the main GH actuators. Thereby, SO might not generate an activation that would prevent the humerus from sliding under the deltoid elevating action. From a stability perspective, the SO shoulder model was the most vulnerable at the end of the concentric phase along the superior-inferior direction, which was not found in our previous study based on SO [26]. The MTUs definition and the analyzed task are relatively similar between both studies. However, [PERSON] et al. [26] optimized the maximal isometric forces to track the experimental EMG (genetic algorithm combined with SO), which partly resembles our calibration step. Accordingly, implementing SOcal, the GH instability decreased, despite the use of static optimization to predict activations. This emphasizes the importance of a subject-specific MTU model for studying complex systems such as the shoulder.  
The GH joint reaction forces predicted with the EMGA method were on the boundary of the non-dislocation cone obtained from the ratios reported on cadaveric data when a compressive force of 50 N was applied [1]. The ratios are expected to decrease as the compressive force is increasing [53]. However, as far as we know, no data are reported in the literature for our complete range of compressive forces (366-2813 N). Additionally, these ratios were obtained when the humerus was positioned in the center of the glenoid, with the arm in a neutral position only, whereas these ratios have been reported to be a function of the joint's 3-DOF [53]. Finally, our model did not include the biceps long tendon. This tendon was reported to oppose the luxation of the humerus as the biceps is contracting [36], thus this contribution might account for the increased risk of dislocation observed in our study.