Introduction: How the CNS activates muscles to produce coordinated movement is a matter of debate and great interest. We are attempting to answer this question, in part, by investigating how individual muscles and groups of muscles are activated under different physiologic and environmental conditions. We have developed an EMG-driven virtual arm to assist in this endeavor.
Purpose: To demonstrate how the virtual arm can be used to simulate a neuromuscular injury and to examine whether a virtual injury can evoke a change in muscle activation patterns.
Methods: The virtual arm is a three-dimensional graphical representation and biomechanical model of a human arm including the major flexor and extensor muscles crossing the elbow. The muscles are actuated based on experimentally recorded electromyograms. A Hill-type muscle model was used to predict muscle forces, which in turn were used to move the graphical display of the arm on the screen. Two subjects, one considered highly trained and the other a novice, participated in this study. Virtual movements, before and after simulating an injury were evaluated, and model performance was assessed by comparing the virtual arm-predicted moment and the actual moment generated by the subjects.
Results: The highly trained subject was proficient at controlling the virtual arm. For this subject, simulating a neuromuscular injury evoked a different pattern of activation compared to the preinjured state.
Conclusions: The virtual arm may be a useful tool for the study of motor coordination and how muscle activation patterns change in response to injury. Future work involving more subjects and experimental conditions is planned to better assess the efficacy of the virtual arm as a research tool for investigating motor control strategies.