A novel methodological framework for the assessment of the neural control of the shoulder using high-density surface electromyography

The complex function of the shoulder relies on the coordinated activation of small and large muscles, including the deltoid, pectoralis major, trapezius, and latissimus dorsi. However, detailed knowledge of their neuromuscular control remains limited. This study aimed to develop a methodological framework to investigate the neural control of the larger superficial shoulder muscles by combining a six-degree-of-freedom load cell attached to a robotic arm with high-density surface electromyograms (HDsEMG). Six healthy participants performed isometric contractions (abduction, adduction, flexion, and extension) at 30% of maximal voluntary contraction with the shoulder positioned at 30° and 65° of lateral abduction. HDsEMGs were recorded from the four muscles and analysed at the global activation, spatial distribution of activation and motor unit levels. Global activation was quantified using averaged normalized root-mean-square (RMS) amplitude and spatial distribution using coefficient of variation of the topographic maps. Moreover, HDsEMGs were decomposed into individual motor unit spike trains using convolutive blind source separation, and motor unit behaviour was characterized by mean discharge rate and spatial distribution of motor unit action potentials (MUAPs). RMS maps revealed action-specific activation within and between muscles, with the upper trapezius active across all tasks, while the anterior, middle, and posterior deltoid, clavicular pectoralis major, and latissimus dorsi were predominantly activated during abduction, flexion, and extension. Motor unit discharge rate also showed task-dependent activity. MUAP spatial distributions further showed distinct motor unit territories within arrays, suggesting region-specific recruitment strategies across actions. In conclusion, this framework demonstrates that individual motor unit activity can be reliably measured non-invasively in the superficial shoulder muscles. The approach provides a methodological basis for novel incorporation of neural control information into biomechanical models of shoulder function.