Biofeedback driven muscle coordination

Groups of muscles are recruited in various combinations to perform smooth, controlled limb movements. Within these muscle groups, the excitation of a single muscle is commonly the focus of manipulation when attempting to influence limb movement, but it is the coordinated excitation of multiple muscles (muscle coordination) that ultimately determines the limb movement and mechanics. Despite successes with single muscles, the capabilities of manipulating muscle coordination are unknown. Therefore the goal of this research was to develop a biofeedback tool to purposefully manipulate muscle coordination.This research is comprised of four studies, three studies facilitated the development of a biofeedback tool and a fourth study finalized the development and validated the capabilities of the tool to purposefully manipulate muscle coordination in real-time during movement. The first study established a physiologically relevant outcome to be used by the biofeedback tool. The study showed that muscle excitation provides good predictions of changes in metabolic power and could therefore be used to determine the relative mechanical efficiency of different muscle coordination strategies.The second and third studies established a muscle coordination reference frame, specific to the relative efficiency outcome ascertained in the first study, used to characterize the current and desired end states of muscle coordination. Specifically, muscle coordination patterns, and their associated relative efficiencies, were determined across a range of mechanical demands to distinguish the key features responsible for differences in relative efficiency that were subsequently used to guide the biofeedback tool.In the final study, a novel biofeedback tool for manipulating muscle coordination was developed and validated. The underlying algorithm used principal component decomposition of muscle excitation to characterize the changes in coordination between the muscles at different mechanical demands. The algorithm was modified to render it feasible for implementation in real-time and the tool was validated by having subjects cycle while receiving feedback comparing their muscle coordination to the reference frame. The results showed that the subjects were successfully able to manipulate muscle coordination to improve the relative efficiency of the movement. Taken together, this research provides a valuable tool for research into motor learning and could be applied to improve rehabilitation and sport performance.