A major issue in rehabilitation robotics is that devices like exoskeletons and treadmills correct patients' movements only while they are using the device. This lack of generalization of motor learning limits the efficacy of robotic interventions. The proposed work will investigate how to manipulate robotic-assisted motor learning to increase its generalization to natural movements in unimpaired people and post-stroke patients. The research has broad impact to public heath because it aims to guide the use of technology for effective gait rehabilitation after stroke, which is the leading cause of long-term disability in the United States. In addition, the PI will use the research objectives in this proposal as a means to increase the participation of students from under-represented groups in science and engineering by recruiting and mentoring undergraduate students from Hispanic-serving universities in Puerto Rico to pursue graduate training. She will also incorporate her research activities with the INVESTING NOW and Pitt EXCEL programs at The University of Pittsburgh. These programs prepare high school students from under-represented groups to pursue degrees in science and engineering and mentor them during their undergraduate studies to ensure their success.
Split-belt walking, in which one leg moves faster than the other, has been shown to induce locomotor learning in the unimpaired and in post-stroke patients. The PI will use analytical tools to characterize the statistics of movements when walking on the treadmill vs. over ground. The empirical studies will determine if the learning phase on the split-belt treadmill can be altered to enhance the generalization of learned movements to natural walking. The research will be informative about motor learning mechanisms available to patients post-stroke and will suggest ways to improve their mobility beyond the clinical setting.
