Title: Neural Oscillations in the Control on Intermuscular Cocontraction
Dr. Shinohara, ClementsAdvisor
Dr. Anderson, Chair
The objective of the proposed research is to investigate neural oscillations for improving concurrent activation control around elbow joint muscles with direct application to Human Robot Interaction (HRI). It is divided into two tracks: 1) study the association between neuromuscular adaptations and system performance for human movement control and 2) develop methods to assess and classify an operator’s skill level and task-dependent physical states from physiological measures during interaction with an external object in a closed loop system. Our primary research suggests that individuals with less correlated neural oscillations (decoupled muscles) tend to perform steady cocontraction more skillfully. It is not clear however if such relationship still exists in a dynamic setting for steady unintentional activations, or whether similar neural processes remain the driving force during faster, transient concurrent contractions. More importantly, a unified framework needs to be developed to extract relevant features for optimal assessment and decision making. We follow a three steps approach to build our framework: 1- We designed and collected data from two integrative experiments (a basic controlled research, and a real life engineering application of HRI) with a total of 78 subjects completing various tasks under different constraints. 2- We will explore the robustness of our primary finding of muscle correlated neural oscillations under different conditions (static, dynamic, and transient coactivation). 3- We will infer a generalized or task-specific framework to assess, classify, and integrate the results.