MEAM Ph.D. Thesis Defense: “Leveraging Robot-Based Haptic Dyads to Improve Community-Based Stroke Rehabilitation”

About this event

Disabilities, including those due to a stroke, are common among older adults. As the world’s population ages, the need for effective, affordable, accessible rehabilitation will increase. Haptically connected individuals in a multiplayer game may experience the social and motivational advantages as well as an implicit communication channel with their partner. The goal of this dissertation 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 robots.

The first specific aim was to leverage an affordable robotic rehabilitation platform to study how age- and stroke-related motor and cognitive impairments influence motor learning when individuals were haptically connected to a partner. Healthy older adults and older adults with stroke learned a robot-based motor task with a 1-week follow-up assessment. The findings of this study suggest that people with stroke did not slack when haptically connected to a partner and that training with a haptic connection to a partner improved motor learning retention for the less skilled partner in the dyad. The second aim was to develop an optimal control model of sensorimotor communication that accounted for motor and cognitive impairments. This model elucidated how age- and stroke-related motor and cognitive impairments mediate tracking behavior. Furthermore, this model provided insight into haptic interactions between healthy and post-stroke participants in dyads.

The third aim was to develop an adaptive dyadic controller that balanced differing partner ability levels in a robot-based haptic dyad. The resulting interaction-as-needed controller was stable and feasible for balancing work between healthy and post-stroke participants. A pilot study demonstrated feasibility of peer-supported robot-assisted rehabilitation. Finally, I analyzed user experience of the multi-user robot system and assessed its readiness for clinical and community-based translation.

This work helps answer fundamental questions about how motor and cognitive impairments influence sensorimotor communication, providing design insight for robot-assisted rehabilitation. The resulting dyadic rehabilitation robot system has the potential to enable individuals with stroke to interact with friends, family, and caregivers in support of their rehabilitative efforts.

From Mechanical Engineering and Applied Mechanics