# EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
## 1 Introduction  
The glenohumeral (GH) joint is the most mobile joint of the human body. This mobility is enabled by the minimal congruence between the glenoid cavity and the humeral head, which compromises the GH stability. This stability requirement is largely met by the scapulohumeral muscular balance [1]. This balance is ensured when the net joint reaction force is directed towards the glenoid fossa, which requires a fine-tuned coordination between shoulder muscles [2]. While rotator cuff muscles have been reported to act as the GH joint main stabilizers, all shoulder muscles contribute to the GH stability [3]. This intricate stability design puts the GH joint at a higher risk of dislocation, and its muscles at a higher injury hazard. As shoulder pathologies have been linked to numerous occupational settings (e.g., repetitive, high force demand, computer set up), and their impact on quality of life has been reported, it is critical to understand these dysfunctions mechanisms [4]. Accordingly, the prediction of shoulder muscle interactions is a key factor in the development of ergonomic interventions. While musculoskeletal modelling enables a non-invasive prediction of these forces, it is hindered by the difficulties related to the redundancy of the shoulder musculoskeletal system. The most common method to estimate shoulder muscle activations is the static optimization of various cost functions, with or without a stability constraint for the GH joint [5; 6; 7; 8], such as the sum of activations or the energetic cost, which is expected to provide more physiological results [9]. However, the inaccuracies associated with static optimization, mainly due to its assumption of a unique optimal control strategy across individuals [10], led many investigators to consider using electromyographic (EMG) data to inform computations, rather than just for validation [11]. EMG-informed modelling methods have been implemented successfully within the calibrated EMG-informed neuromusculoskeletal modelling toolbox (CEINMS [12]) for OpenSim [13]. These methods seek to track simultaneously the experimental EMG and the various joint moments, in order to predict muscle activation. Additionally, they include a model calibration step, where, the experimental EMG is used as input, and the joint moments are tracked to fine-tune the muscle modelling parameters. CEINMS has been implemented for lower limb joints [12; 14], where the co-contraction predicted by this toolbox was consistently higher than the one predicted by static optimization. Additionally, the predicted knee joint reaction forces were similar to experimental ones. This toolbox has also been implemented in its EMG-driven mode for shoulder analysis during isometric tasks [15], where it was concluded that a common cost function underestimates muscle co-contraction in comparison to an EMG-driven approach. However, the EMG-driven mode had already proven its limitations in the analysis of the hip joint due to the insufficient experimental data [12; 16]. Additionally, the use of this mode did not account for artefacts related to the envelop extraction [16; 17]. Indeed, the implementation of EMG-informed algorithms faces a number of limitations, that are particularly true for the shoulder. The first one being the presence of deep musculature [18] that limits the number of available experimental EMG. The second limitation is related to the quality of the EMG data, particularly because of artefacts introduced with dynamic tasks and envelope extraction. To overcome the first limitation, indwelling electrodes should be used where possible, and unattainable EMG data should be synthetized. As for the second limitation, the input data should be adjusted to account for joint moment tracking errors. Accordingly, we implemented a shoulder model using OpenSim [13; 19] and the EMG-adjusted algorithm of CEINMS [12] for the analysis of a relatively complex dynamic task. Our objective is to predict muscle activation patterns that could account for the stabilizing role of shoulder muscles.