CAREER: Integrating Sensorimotor Models into Human-Robot Collaboration in Gait, Posture, and Unsteady Tasks

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This Faculty Early Career Development (CAREER) grant supports research that will contribute new knowledge to the design and control of wearable robotic devices for assisting everyday movements, such as walking and standing balance. Wearable robotic devices, such as exoskeletons, can be used to provide assistive forces to those who have impaired mobility or other disabilities. Although the symptoms driving disability are varied and specific to the individual, current robots do not yet have the intelligence to modify their behavior based on the user’s specific needs. This award supports fundamental research on methods to use measurements of specific deficits in the user’s primary senses to improve how humans and robots collaborate during movement tasks. The results of this research will advance interdisciplinary knowledge in robotics, biomechanics, neuroscience, and control. Reducing the impact of disability and helping people return to work will greatly benefit the U.S. economic and societal goals to advance science and promote human health. The broader impacts of this work include training and research partnerships with local high schools and a nearby historically black university to attract and retain women and underrepresented minorities. To estimate the body’s movement, humans use three primary sensory systems: visual, vestibular, and somatosensory. A major element of the brain’s ability to control movement is to use information from multiple sensory systems to improve the ability to estimate self-motion and sensory feedback. When one of the sensory systems experiences a dynamic loss of accuracy, such as a step transition into soft ground or a loss of vision, the brain compensates by reducing the contribution of the impacted sense to the overall estimation in a process called sensory reweighting. This fundamental research in robot-assisted gait and posture will advance scientific understanding by studying how machines affect the dynamics of the perception of self-motion, cognition, and motor control in the presence of sensory deficits. In the first objective, the researchers will perform experiments with the exoskeleton and virtual reality to identify how exoskeletons affect the sensory reweighting processes in self-paced walking and standing balance. These results will provide novel measurements of the physical and sensory dynamics of human-robot collaboration. In the second objective, they will develop methods to incorporate individualized perceptual models of sensory reweighting into simulations of walking and standing. In the final objective, they will explore methods for real-time detection of sensory reweighting and the usage of haptic feedback as a tool to communicate state and trust between person and machine.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.