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

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 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.

Brain-inspired semantic data augmentation for multi-style images

“…Robust statistics seek to provide methods that emulate popular statistical methods, but are not excessively affected by outliers or other small departures from model assumptions (Maronna et al, 2019). Robust statistics can be utilized to detect the outliers by searching for the model fitted by the majority of the data (Rousseeuw and Hubert, 2011;Feldotto et al, 2022). There are efficient robust estimators for a series of complex problems, including covariance estimation (Cheng et al, 2019;Diakonikolas et al, 2019a), sparse estimation tasks (Balakrishnan et al, 2017;Diakonikolas et al, 2019c;Cheng et al, 2022), learning graphical models (Cheng et al, 2018;Diakonikolas et al, 2021), linear regression (Klivans et al, 2018;Diakonikolas et al, 2019d;Pensia et al, 2020), stochastic optimization (Diakonikolas et al, 2019b;DeWolf et al, 2020;Prasad et al, 2020), etc.…”