Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement

Abstract

Muscle coordination may be difficult or impossible to predict accurately based on biomechanical considerations alone due to redundancy in the musculoskeletal system. Because many solutions exist for any given movement, the role of the nervous system in further constraining muscle coordination patterns for movement must be considered in both healthy and impaired motor control. Based on computational neuromechanical analyses of experimental data combined with modeling techniques, we have demonstrated several such neural constraints on the temporal and spatial patterns of muscle activity during both locomotion and postural responses to balance perturbations. We hypothesize that subject-specific as well as trial-by-trial differences in muscle activation can be parameterized and understood by a hierarchical and low-dimensional framework that reflects the neural control of task-level goals. In postural control, we demonstrate that temporal patterns of muscle activity may be governed by feedback control of task-level variables that represent the overall goal-directed motion of the body. These temporal patterns then recruit spatially-fixed patterns of muscle activity called muscle synergies that produce the desired task-level biomechanical functions that require multi-joint coordination. Moreover, these principles apply more generally to movement, and in particular to locomotor tasks in both healthy and impaired individuals. Overall, understanding the goals and organization of the neural control of movement may provide useful reduced dimension parameter sets to address the degrees-of-freedom problem in musculoskeletal movement control. More importantly, however, neuromechanical analyses may lend insight and provide a framework for understanding subject-specific and trial-by-trial differences in movement across both healthy and motor-impaired populations.

A Trap Motion in Validating Muscle Activity Prediction from Musculoskeletal model using EMG

“…This information is useful for future validation study or for a clinical use that the muscle activity from the model should be calculated about 20 ms later compare to EMG. The existance of delay is confirmed well by the previous review study done by [28]. In that previous study, some adjusment strategy need to be performed when comparing EMG and muscle activity prediction from the model.…”

Analysis of muscle synergies and activation–deactivation patterns in subjects with anterior cruciate ligament deficiency during walking

“…This decomposition allows to obtain positive values for both NCs and SVs, what leads to a more physiological interpretation (Ting et al, 2012). The maximum value of each SV was constrained to be 1 to decrease the indeterminacy of the factorization.…”

“…Subjects with ACL deficiency alter their muscle activations when doing a certain task due to the lack of ACL. It is believed that muscles are activated synergistically following a certain pattern depending on the motor task (Lacquaniti et al, 2012;Ting and McKay, 2007;Ting, 2007;Ting et al, 2012), that is to say, our Central Nervous System (CNS) does not activate the muscles independently. Muscle synergies are represented by modules consisting of one Neural Command (NC), which represents the time activation of a set of muscles, and one Synergy Vector (SV), which represents the weighting factor of each muscle to its NC (Ting and Macpherson, 2005).…”

Application of robotic manipulability indices to evaluate thumb performance during smartphone touch operations

“…In recent years, evaluation of motion range, estimation of muscular and articular forces and generation of movement have been performed with digital human modelling (DHM) (Morrow, Kaufman, and An 2010;Sartori et al 2013;Ting et al 2012). Physiological effects that were previously evaluated by human electromyography can now be assessed based on muscular and articular forces estimated from DHM.…”