Jing Feng, Ph.D. - US grants

This grant will support research that will contribute new knowledge related to human-prosthesis interactions and the personalization of robotic prosthesis control. Current powered lower limb (LL) robotic prostheses use intrinsic feedback of joint motion and forces to adjust joint impedance as a function of the current phase of gait. Personalization of the control parameters (controller tuning) is typically performed manually and heuristically by a clinician who modifies one parameter at a time until the amputee's gait "looks good" and the amputee self-reports satisfaction with the control. The research objective of this project is to develop a novel "wearer-led" auto-tuning procedure for LL robotic prostheses that considers and enhances cognitive aspects of the human-machine interaction during gait, such as the user's goals, required attention, cognitive workload, trust and comfort. This project will promote the progress of science and advance the national health by developing intelligent prosthesis controllers that can tune themselves to the personal preferences and perceptions of their users, thereby augmenting the physical performance of individuals with lower limb amputations and maximize their acceptance of the robotic limbs as a functional part of the body. Broader impacts of the project include K-12 outreach and educational efforts at the undergraduate and graduate levels intended to attract and retain women and underrepresented minorities into STEM fields.

The project involves three sets of research activities aimed at developing a user-adaptive auto-tuning procedure for lower limb robotic prostheses. The first develops a novel "wearer-led" tuning procedure wherein able-bodied subjects and individuals with unilateral trans-femoral amputation will walk with the robotic prosthesis and tune its control parameters using a "think aloud" process. The research team will perform human subject experiments to study the wearers' thought and decision processes as they walk with a robotic limb and adjust its control parameters to optimize its performance and comfort. The second set compares physical gait performance and selected aspects of cognitive functioning while walking with a prosthesis that is tuned either by an existing machine learning algorithm, by a trained prosthetist, or using the novel wearer-led tuning procedure. Biomechanical performance and measures of perception, cognitive workload, attentional allocation, and other cognitive factors will be compared across the three tuning approaches. The results will fill the knowledge gap in wearer-robot interactions at the cognitive and physical levels. The third set of activities will incorporate the novel wearer-led tuning procedure into a new, intelligent, automated, lower limb prosthesis tuning system that directly incorporates the wearer's preference, perception, and cognition into the procedure for prostheses personalization, thereby promoting seamless wearer-prosthesis integration and embodiment.

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.