Evaluating Muscle Synergies with EMG Data and Physics Simulation in the Neurorobotics Platform

TL;DR

This paper introduces a novel physics simulation framework using EMG data in the Neurorobotics Platform to analyze muscle synergies and their contribution to joint torque, enhancing understanding of limb control.

Contribution

The framework combines EMG-driven musculoskeletal modeling with genetic algorithms in a modular platform to evaluate muscle contributions and simulate alternative activation patterns.

Findings

Generated torque patterns matching human data

Identified muscle synergies similar to biological ones

Discovered alternative activation patterns producing same torque

Abstract

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.