Leveraging Robot-Based Haptic Dyads to Improve Community-Based Stroke Rehabilitation

PROJECT SUMMARY/ABSTRACT Disabilities, including those due to a stroke, are common among older adults worldwide, affecting about 36% of adults 65 and older in the USA.1 As the world’s population ages, the need for effective, affordable, accessible rehabilitation will increase. This need is particularly acute in low and middle income countries (LMICs), which carry 90% of the global stroke burden.2 Limited healthcare resources in LMICs necessitate practical solutions such as community-based rehabilitation and affordable robotics that allow caregivers to help with rehabilitation. My goal is to improve community-based robotic therapy by implementing a joint learning paradigm for individuals with varying levels of motor and cognitive impairment. Haptic interaction or the transmission of tactile information using sensations such as vibration, touch, and force feedback between individuals can improve rehabilitation. Haptically connected individuals in a multiplayer game may experience the social and motivational advantages as well as the implicit communication channel afforded by a haptic connection to a partner. The goal of this project is to determine how individuals with varying motor and cognitive impairments communicate and learn during haptic interaction in order to better design haptic feedback for multiplayer rehabilitation robot games. The rst specic aim is to leverage an affordable robotic rehabilitation platform to study how age and stroke-related motor and cognitive impairments inuence motor learning when individuals are haptically connected to a partner. Healthy older adults and older adult stroke survivors will learn a robot-based motor task with a 1-week follow-up assessment. I expect that a haptic connection to a partner with similar or less motor impairment will result in greater motor learning, especially for those with age or stroke related cognitive impairments, than learning individually. I also expect that a haptic connection to a partner with greater motor impairment will reduce motor learning. The second aim is to develop a model of sensorimotor communication using inverse optimal control techniques that accounts for motor and cognitive impairments. This model will reveal how age and stroke related motor and cognitive impairments mediate different sensory feedback channels (e.g., visual, haptic). Finally, the third aim is to develop an adaptive dyadic controller that balances differing partner ability levels in a robot-based haptic dyad. This adaptive dyadic rehabilitation robot will enable older adults with motor and/or cognitive impairments to interact and support each other’s rehabilitative efforts. This project will help answer fundamental questions about how motor and cognitive impairments inuence sensorimotor communication, providing design insight for robotic rehabilitation. Done in the context of a pre-doctoral training plan, this work, which helps to develop an independent researcher at the intersection of robotics and rehabilitation science, will be completed within Mechanical Engineering, Physical Medicine and Rehabilitation, and the General Robotics, Automation, Sensing, and Perception (GRASP) laboratory at the University of Pennsylvania.