Investigating features in augmented visual feedback for virtual reality rehabilitation of upper-extremity function through isometric muscle control
Sean Sanford, Brian D. Collins, Mingxiao Liu, Sophie Dewil, Raviraj Nataraj
Abstract
Previous studies have demonstrated how augmented feedback can accelerate motor learning. Still, how specific feedback features of complexity and intermittency can influence learning a challenging, force-driven motor task remains largely unknown. This study is an initial investigation of how variations in the complexity and intermittency of augmented visual guidance affect the performance of an isometric muscle control task with a computerized platform. This novel platform has been developed to rehabilitate upper-extremity function after neuromuscular dysfunction (e.g., spinal cord injury, stroke) while utilizing: 1) a position-adjustable arm brace for gravity support; 2) a myoelectric command interface; 3) virtual reality (VR) for motor training. Results from this study elucidate new motor control principles and suggest how augmented guidance may be leveraged in designing VR motor rehabilitation programs, which are highly flexible and customizable to individual users. This study demonstrated that simpler and more intermittent feedback typically resulted in better performance (i.e., shorter computerized motion pathlengths). Supplementary results suggested these feedback modes also reduced cognitive loading (i.e., alpha/beta band magnitudes in electroencephalography) but increased physical arousal (i.e., higher skin conductance). In sum, this study indicates that for complex, force-driven tasks, augmented guidance must be presented selectively to accelerate gains in motor performance. This study suggests that simple and intermittent feedback avoids cognitively overwhelming the user while encouraging physical engagement that supports better performance.