Understanding and Enhancing Proprioception via Model-Based Human-Robot Interactions

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Proprioception is the sense of body position and movement that comes from specialized sensors in the joints and muscles of the body. Proprioception is impaired on one side of the body in about half of stroke survivors. The research objective of this project is to develop personalized robotic interventions targeting upper extremity rehabilitation for people with proprioceptive impairments after neurological injury such as stroke. The project will conduct human subject experiments, develop novel computational models, and use machine learning methods to enhance the design of personalized, subject-adaptive robotic interventions that are optimized for each individual's specific profile of impairment. This project will promote the progress of science and advance national health by developing new robotic therapies that have the potential to improve quality of life for a large number of people with sensorimotor impairments after stroke. Additionally, the work may increase national prosperity by reducing the healthcare cost burden of stroke through optimization of neurorehabilitative techniques and their robotic delivery. Broader impacts of the project include efforts to expose a diverse group of high school students to biomechanics through activities associated with National Biomechanics Day and a workshop to be developed.This project will examine how humans combine visual and proprioceptive information to estimate upper limb position, and then use these data to guide the development and testing of robot-aided rehabilitation in stroke survivors. The project will conduct three sets of activities: 1) human subject experiments exploiting human-robotic interactions to characterize stroke-related deficits of multisensory integration during the assessment of limb posture and movement; 2) the development of novel extensions to Bayesian models of multi-sensory integration and evaluation of their ability to describe impaired multisensory cue combination post-stroke; and 3) the design and implementation of individualized, robot-assisted, subject-adaptive training algorithms to rehabilitate multisensory function in upper limb impairment after stroke. This project advances the M3X program vision because it exploits human-robot interactions not only to interrogate the sensorimotor state of individuals after stroke, but to also drive improvement in sensorimotor function using an individualized robotic training approach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.