The influence of proprioceptive feedback on muscle activity during isometric tasks is the subject of conflicting studies. We performed an isometric knee extension task experiment based on two common clinical tests for mobility and flexibility. The task was carried out at four pre-set angles of the knee and we recorded from five muscles for two different hip positions. We applied muscle synergy analysis using non-negative matrix factorisation on surface electromyograph recordings to identify patterns in the data which changed with internal knee angle, suggesting a link between proprioception and muscle activity. We hypothesised that such patterns arise from the way proprioceptive and cortical signals are integrated in neural circuits of the spinal cord. Using the MIIND neural simulation platform, we developed a computational model based on current understanding of spinal circuits with an adjustable afferent input. The model produces the same synergy trends as observed in the data, driven by changes in the afferent input. In order to match the activation patterns from each knee angle and position of the experiment, the model predicts the need for three distinct inputs: two to control a non-linear bias towards the extensors and against the flexors, and a further input to control additional inhibition of rectus femoris. The results show that proprioception may be involved in modulating muscle synergies encoded in cortical or spinal neural circuits.

Proprioceptive feedback and its role in control of isometric tasks is often overlooked. In this study recordings were made from upper leg muscles during an isometric knee extension task. Internal knee angle was fixed and subjects were asked to voluntarily activate their rectus femoris muscle. Muscle synergy analysis of these recordings identified canonical temporal patterns in the data. These synergies were found to encode two separate features: one concerning the coordinated contraction of the recorded muscles and the other indicating agonistic/antagonistic interactions between these muscles. The second synergy changed with internal knee angle reflecting the influence of afferent activity. This is in contrast to previous studies of dynamic task experiments which have indicated that proprioception has a negligible effect on synergy expression. Using the MIIND neural simulation platform, we developed a spinal population model with an adjustable input representing proprioceptive feedback. The model is based on existing spinal population circuits used for dynamic tasks. When the same synergy analysis was performed on the output from the model, qualitatively similar muscle synergy patterns were observed. These results suggest proprioceptive feedback is integrated in the spinal cord to control isometric tasks via muscle synergies. Significance statementSensory feedback from muscles is a significant factor in normal motor control. It is often assumed that instantaneous muscle stretch does not influence experiments where limbs are held in a fixed position. Here, we identified patterns of muscle activity during such tasks showing that this assumption should be revisited. We also developed a computational model to propose a possible mechanism, based on a network of populations of neurons, that could explain this phenomenon. The model is based on well established neural circuits in the spinal cord and fits closely other models used to simulate more dynamic tasks like locomotion in vertebrates.

Although we can measure muscle activity and analyze their activation patterns, we understand little about how individual muscles affect the joint torque generated. It is known that they are controlled by circuits in the spinal cord, a system much less well-understood than the cortex. Knowing the contribution of the muscles toward a joint torque would improve our understanding of human limb control. We present a novel framework to examine the control of biomechanics using physics simulations informed by electromyography (EMG) data. These signals drive a virtual musculoskeletal model in the Neurorobotics Platform (NRP), which we then use to evaluate resulting joint torques. We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model. The resulting knee torques are used as a reference for genetic algorithms (GA) to generate new simulated activation patterns. On the platform the GA finds solutions that generate torques matching those observed. Possible solutions include synergies that are similar to those extracted from the human study. In addition, the GA finds activation patterns that are different from the biological ones while still producing the same knee torque. The NRP forms a highly modular integrated simulation platform allowing these in silico experiments. We argue that our framework allows for research of the neurobiomechanical control of muscles during tasks, which would otherwise not be possible.

Although we can measure muscle activity and analyze their activation patterns, we understand little about how individual muscles affect the joint torque generated. It is known that they are controlled by circuits in the spinal cord, a system much less well understood than the cortex. Knowing the contribution of the muscles towards a joint torque would improve our understanding of human limb control. We present a novel framework to examine the control of biomechanics using physics simulations informed by electromyography (EMG) data. These signals drive a virtual musculoskeletal model in the Neurorobotics Platform (NRP), which we then use to evaluate resulting joint torques. We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model. The resulting knee torques are used as a reference for genetic algorithms (GA) to generate new simulated activation patterns. On the platform the GA finds solutions that generate torques matching those observed. Possible solutions include synergies that are similar to those extracted from the human study. In addition, the GA finds activation patterns that are different from the the biological ones while still producing the same knee torque. The NRP forms a highly modular integrated simulation platform allowing these in silico experiments. We argue that our framework allows for research of the neurobiomechanical control of muscles during tasks, which would otherwise not be possible.