Control of Human Arm Movement

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Humans perform a wide variety of skilled motor tasks, from writing an autograph to riding a bicycle, yet, understanding how humans plan and control even simple movements is still a major chal- lenge in neuroscience. A large number of joints and muscles provides the central nervous system (CNS) with many degrees of freedom in movement and control; movements can be achieved by an infinite number of joint rotations and muscle activation patterns. Surprisingly, research has revealed consistent, stereotypical patterns of movement and muscle activation, both within and across individuals, but the question of how the CNS controls these many degrees of freedom is still a key unresolved problem in motor control and sensory-motor neuroscience. In this proposal we describe studies aimed at directly testing hypotheses about how the CNS controls movement, within the current major computational framework of motor control and motor learning, namely optimal feedback control (OFC). Our approach combines human empirical work using custom-designed, high performance robotic devices to both measure and perturb arm movements, with computer simulations using physiologically realistic computational models of the arm neuromuscular system to generate specific predictions. We use the robots to deliver precise motion-dependent loads to the arm during visually-guided reaching movements, and a virtual display system to deliver visuo-motor perturbations, by manipulating the relationship between the unseen hand and a cursor representing hand position. A combination of force-fields and visuo-motor perturbations are used to test hypothesis about the putative cost function for motor learning. We will also empirically test the hypothesis that metabolic energy is minimized by the CNS, by using expired gas analysis to measure VO2 changes over the course of motor learning. This research will expand our knowledge of how voluntary movements are planned and controlled, and how motor learning is achieved by the brain. The results of this work also has the potential to advance fields in applied engineering and technology such as neural prosthetics, human-machine interfaces and anthropomorphic robotics.