Exploration of optimal prosthesis feedback information using computational motor control

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Better robotic prostheses can dramatically improve the quality of life for persons with an upper limb amputation, many of whom reject existing devices because they have trouble controlling them in the same intuitive, subconscious way that they controlled their intact arms. Prosthesis control is difficult because amputees experience great uncertainty both with respect to whether their device will respond appropriately to their control signals and whether sensory feedback cues accurately reflect the actual movement. Researchers have focused on improving isolated aspects of control, for example by improving filters or mimicking able-bodied sensory cues through haptic devices, but these approaches have minimally reduced the uncertainty of prosthesis control. Human interaction with a prosthesis is a multifaceted, time-varying problem that is difficult to solve. What is missing from robotic prosthesis research are principled methods for optimizing control strategies and sensory cues that take into account behavioral choices people are known to make in the face of high uncertainty. **Our unique approach is to use an optimization strategy that incorporates the behavioral decisions that humans intuitively make in order to deal with uncertainty. For healthy subjects, computational motor control models based on human behavioral data describe very well how subjects learn, estimate, and control. This is even true for cases that are analogous to prosthesis use, such as mapping non-intuitive joints to abstract degrees of freedom or signal-dependent noise. This suggests that those models could also predict how an amputee learns to control a prosthesis using their noisier control signals and limited sensory feedback. Building models and calibrating them with experiments to make them predictive will allow us to study how different decoder and feedback designs would affect behavior. This model-driven approach promises faster and more efficient prosthesis design. We plan to quantify the uncertainty that amputees attribute to various sources (their control signals, the prosthesis, and the world); to develop novel controllers that reduce the uncertainty of control; and to provide haptic sensory cues that work synergistically with available sensors and control strategies to reduce uncertainty. **The proposed research is innovative because it poses the control problem in a broader context that incorporates the highly sophisticated behavioral decisions that humans make in optimizing their control strategy and sensory cues. This approach is able to integrate multiple effects in ways that were not possible using previous approaches. For example, our approach naturally incorporates the fact that people prefer to use less exerted effort to accomplish a task, but tolerate more effort during portions of movement that require greater precision (e.g. final portion of a trajectory). On the other hand, our approach does not favor high-certainty haptic cues if those cues provide redundant information to existing sensory cues such as vision, or if the haptic information does not reduce the uncertainty of controllable system dynamics. Due to the large sources of control-signal noise present in amputees, our work will lead to improved techniques within the fields of computational motor control and optimal control. This research builds on our team's extensive experience in the design and control of upper-limb prostheses and our collaborator's experience in the field of computational motor control. Achievement of the proposed aims will contribute to the field of robotic control and to such diverse fields as human-robot interaction, perception, manipulation, and exoskeletons, and will provide a rich platform for education at all levels.