The Effect of Muscle and Kinematic Complexity on Feasible Forces and Muscle Activations in a Model of the Human Leg
Smith, Daniel Michael
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Muscles move and stabilize the body. People with motor deficits, regardless of the deficit’s origin, have less control and/or worse coordination or their muscles which seriously affects their quality of life and ability to do basic tasks for themselves. While different methods of rehabilitation and therapy can help those with motor deficits increase their motor control, the ability to improve upon these efforts is limited by an incomplete understanding how muscle activity is coordinated. The degree to which muscle activity is determined by neural selection or by biomechanics is unresolved; musculoskeletal redundancy allows for variations in the muscle activation patterns used to control movement, but it is unknown how much this redundancy constrains or allows variations in muscle activity. Computational musculoskeletal models are commonly used to quantify redundancy, but contradictory results are found in the literature: some studies suggest wide feasible ranges of muscle activity for the nervous system to control, while others suggest biomechanics largely determine muscle coordination. We hypothesized that the contradictory results are because of different numbers of muscles and degrees-of-freedom (DoFs) in the models used, and that models with more realistic complexity allow for more variability in muscle coordination. We tested redundancy by looking at the role of individual muscles for a given task. A redundant muscle shares function with another muscle, and the more function it shares the more redundant it is. For the purposes of this study, we quantified a muscle’s redundancy for a task by measuring the robustness of static force production to the loss of that muscle’s function. To compare a standard detailed musculoskeletal model with a simplified model from the literature, we systematically varied both the number of muscles and kinematic DoFs of a musculoskeletal model and tested the significance of individual muscles in each model by looking at 1) the sensitivity of static force production to single muscle loss via the set of biomechanically feasible forces (feasible force set, FFS) and 2) the feasible ranges of muscle activations (feasible muscle activation ranges, FMARs) at maximum force in the sagittal plane. We demonstrated that results from many studies that used simplified models do not generalize to more realistic systems, while more realistic models suggest that very few muscles are constrained by biomechanics. The sensitivity of the FFS to single muscle loss decreased as model complexity increased, and the robustness of the FFS to general single muscle loss increases as model complexity increased. We also showed muscle activity is often unconstrained even at maximum forces; most muscles exhibited wide FMARs at maximum force in many or most force directions. Only a few muscles (the hip-knee biarticular muscles) were completely constrained for all maximum sagittal plane force directions. Further, we showed that the effects of complexity in muscles and DoFs observed in these cases are general for any musculoskeletal system. When evaluating whether a musculoskeletal model is well-suited to study muscle redundancy, researcher should include in their considerations how well the number of muscles in the model accurately represents the redundancy of what is being modeled, as well as the ratio of muscles to joints. An understanding of the degree to which muscle activity is determined by biomechanics and/or by neural selection has significant implications for rehabilitation. Low levels of biomechanical constraints suggest many different neural strategies or compensations are feasible, indicating rehabilitation efforts should focus on training muscle coordination.