M Wu
BME PhD Defense Presentation

Date: 2024-03-25
Time: 1:30pm

Location / Meeting Link: Georgia Tech MRDC 4211 Conference Room / https://emory.zoom.us/j/97895251894 

Committee Members:
Lena H. Ting, PhD (Advisor); Jun Ueda, PhD; Madeleine E. Hackney, PhD; Gregory S. Sawicki, PhD; Trisha Kesar, PhD


Title: Low-force hand interactions induce changes to gait through human sensorimotor engagement with task-relevant information instead of direct mechanical effects

Abstract:
Low-force hand interactions are a novel approach for improving human walking and enhancing performance of collaborative physical tasks during walking. Current robotic devices such as wearable exoskeletons and robotic walkers aid walking primarily through direct mechanical effects – applying large forces directly on human tissues/joints to propel locomotion or by supporting significant bodyweight. In contrast, examples from human-human pairs – especially partner dancing – show that subtle hand interactions can convey a variety of information to influence how a person controls their own movement. However, the underlying human sensorimotor control strategies are not well understood, and there are no existing hand-contact robotic devices adequate for testing controllers based on human-human hand interactions during walking. To address these scientific and engineering gaps, I develop experiment paradigms, analysis methods, and computational models to characterize human sensorimotor control strategies and create a novel robotic device to evaluate and prototype hand interaction controllers during walking. Through a series of human-human and human-robot studies, I demonstrate my central hypothesis that low-force hand interactions can induce people to change their own gait through sensorimotor engagement with task-relevant haptic information instead of relying on direct mechanical effects. My results provide principles of haptic communication and sensorimotor engagement for physical collaborations between human-human or human-robot partners and contribute to understanding of how hand interactions influence walking within an individual. Such principles can be used to guide design of more effective and intuitive low-force hand interactions for a variety of applications, such as physical assistance and rehabilitation (e.g. robotic walkers), industrial manufacturing (e.g. human-robot load-transportation), physical education (e.g. teaching dance), and recreation.