Reshaping movement distributions with limit-push robotic training
journal contributionposted on 2021-08-16, 16:32 authored by AK Shah, I Sharp, E Hajissa, James PattonJames Patton
High-cost situations need to be avoided. However, occasionally, cost may only be learned by experience. Here, we tested whether an artificially induced unstable and invisible high-cost region, a 'limit-push' force field, might reshape people's motion distributions. Healthy and neurologically impaired (chronic stroke) populations attempted 600 interceptions of a projectile while holding a robot handle that could render forces to the hand. The 'limit-push,' in the middle of the study, pushed the hand outward unless the hand stayed within a box-shaped region. Both healthy and some stroke survivors adapted through selection of safer actions, avoiding the high-cost regions (outside the box); they stayed more inside and even kept a greater distance from the box's boundaries. This was supported by other measures that showed subjects distributed their hand movements within the box more uniformly. These effects lasted a very short time after returning to the no-force condition. Although most robotic teaching approaches focus on shifting the mean, this limit-push treatment demonstrates how both mean and variance might be reshaped in motor training and neurorehabilitation.
CitationShah, A. K., Sharp, I., Hajissa, E.Patton, J. L. (2018). Reshaping movement distributions with limit-push robotic training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(11), 2134-2144. https://doi.org/10.1109/TNSRE.2018.2839565
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Movement variabilityobstacle avoidancestrokesensory motor learningmotor planningAdultAlgorithmsCohort StudiesFemaleHandHealthy VolunteersHumansMaleMiddle AgedMovementPsychomotor PerformanceRecovery of FunctionRoboticsStrokeStroke RehabilitationVirtual RealityYoung AdultBiomedical EngineeringElectrical and Electronic Engineering