Sensitivity of Neuromechanical Predictions to Choice of Glenohumeral Stability Modeling Approach

Does enforcing glenohumeral joint stability matter? A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles

The complexity of the human shoulder girdle enables the large mobility of the upper extremity, but also introduces instability of the glenohumeral (GH) joint. Shoulder movements are generated by coordinating large superficial and deeper stabilizing muscles spanning numerous degrees-of-freedom. How shoulder muscles are coordinated to stabilize the movement of the GH joint remains widely unknown. Musculoskeletal simulations are powerful tools to gain insights into the actions of individual muscles and particularly of those that are difficult to measure. In this study, we analyze how enforcement of GH joint stability in a musculoskeletal model affects the estimates of individual muscle activity during shoulder movements. To estimate both muscle activity and GH stability from recorded shoulder movements, we developed a Rapid Muscle Redundancy (RMR) solver to include constraints on joint reaction forces (JRFs) from a musculoskeletal model. The RMR solver yields muscle activations and joint forces by minimizing the weighted sum of squared-activations, while matching experimental motion. We implemented three new features: first, computed muscle forces include active and passive fiber contributions; second, muscle activation rates are enforced to be physiological, and third, JRFs are efficiently formulated as linear functions of activations. Muscle activity from the RMR solver without GH stability was not different from the computed muscle control (CMC) algorithm and electromyography of superficial muscles. The efficiency of the solver enabled us to test over 3600 trials sampled within the uncertainty of the experimental movements to test the differences in muscle activity with and without GH joint stability enforced. We found that enforcing GH stability significantly increases the estimated activity of the rotator cuff muscles but not of most superficial muscles. Therefore, a comparison of shoulder model muscle activity to EMG measurements of superficial muscles alone is insufficient to validate the activity of rotator cuff muscles estimated from musculoskeletal models.

Neuromuscular control: from a biomechanist's perspective

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.

Development and Assessment of a Method to Estimate the Value of a Maximum Voluntary Isometric Contraction Electromyogram from Submaximal Electromyographic Data

The electromyographic (EMG) normalization (often to maximum voluntary isometric contraction [MVIC]) is used to control for interparticipant and day-to-day variations. Repeated MVIC exertions may be inadvisable from participants' safety perspective. This study developed a technique to predict the MVIC EMG from submaximal isometric voluntary contraction EMG. On day 1, 10 participants executed moment exertions of 100%, 60%, 40%, and 20% of the maximum (biceps brachii, rectus femoris, neck flexors, and neck extensors) as the EMG data were collected. On day 2, the participants replicated the joint moment values from day 1 (60%, 40%, and 20%) and also performed MVIC exertions. Using the ratios between the MVIC EMGs and submaximal isometric voluntary contraction EMG data values established on day 1, and the day 2 submaximal isometric voluntary contraction EMG data values, the day 2 MVIC EMGs were predicted. The average absolute percentage error between the predicted and actual MVIC EMG values for day 2 were calculated: biceps brachii, 45%; rectus femoris, 27%; right and left neck flexors, 27% and 33%, respectively; and right and left neck extensors, both 29%. There will be a trade-off between the required accuracy of the MVIC EMG and the risk of injury due to exerting actual MVIC. Thus, using the developed predictive technique may depend on the study circumstances.