HEBB: Human-Robot Enabled System to Induce Brain Behavior Adaptations

Technology used to promote human neural plasticity and recovery of function following nervous system injury needs to be personalized and account for individual differences in order to facilitate effective behavioral change. However, few systems account for individual differences in learning or plasticity, or support longitudinal, adaptive human motor control. For example, in the domain of neurorehabilitation, there is currently limited evidence that rehabilitation produces meaningful or persistent changes in walking function for individuals following stroke. These findings underscore a significant knowledge gap regarding the capacity for motor adaptation and represent an urgent unmet need obstructing development of intelligent systems designed to promote recovery of function in persons post-stroke.

Thus, the objective of this proposal is to create an embodied, intelligent robotic system that provides personalized, adaptive feedback to induce neuromotor plasticity, mediate motor adaptation, and leverage meaningful, lasting changes in motor function. To meet this goal, we will determine: (1) the forms and timing of performance feedback to optimize motor learning, and (2) the capacity for neuroplasticity to promote meaningful behavioral change; (3) this collective information, combined with machine learning, will be used to personalize the interactions necessary to maximize motor behavioral changes.

Overall, the envisioned system will involve bi-directional learning between human and machine to determine how to prioritize important, but individual-specific variables (e.g., feedback type) critical for maintaining and promoting motor function across the life and health span.