Low-Force Hand Interactions Induce Changes to Human Gait through Sensorimotor Engagement with Task-Relevant Information Instead of Direct Mechanical Effects
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Wu, M.
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Abstract
The motivation for this research is to develop intuitive low-force human-robot hand interactions to alter human walking. These types of interactions have the potential to improve human movement, enhance physical collaborations between human-human and human-robot partners, and facilitate the performance of physical tasks not previously possible. While current physical human-robot interactions aid walking primarily through direct mechanical effects – i.e. by applying large forces directly on human tissues/joints to propel locomotion or by supporting significant bodyweight, I take a different approach to improving walking through low-force interactions at the hands. This approach is inspired by subtle hand interactions between human partners that alter gait without explicit instructions or training. While concepts from human-human interactions have the potential to inspire human-robot interactions, the control strategies for modifying gait in human partners 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.
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. I demonstrate the feasibility for hand interactions to induce intended changes to gait by showing a) improvements to balance and desired changes to step frequency in human-human interactions and b) desired changes to gait coordination in human-robot interactions. I demonstrate that these gait changes do not rely solely on mechanical effects by showing that a) hand forces remain below 30N, b) mechanical power transfer at the hands is not sufficient for directly propelling walking, and c) gait changes occur only when humans expect task-relevant information from hand interactions.
This research establishes a scientific framework for examining human sensorimotor control of hand interactions during walking. I develop novel experimental paradigms, analysis methods, computational models, and a physical device – a robotic emulator – to enable investigations of human control strategies. My results provide principles of haptic communication and sensorimotor engagement for physical collaborations between human-human and human-robot partners as well as greater understanding of how hand interactions influence walking within an individual.
This work also has broad engineering applications for improving human walking and performance of hand interactions during walking. I develop a hand pHRI controller specifically to alter gait coordination and provide general guiding principles for designing effective and intuitive low-force hand interactions during walking. Such controllers have many potential 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.
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2024-03-29
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Dissertation