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 specic aim is to leverage an affordable robotic rehabilitation platform to study how age and
stroke-related motor and cognitive impairments inuence 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 inuence
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.