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The National Science Foundation (NSF) has named the Shirley Ryan AbilityLab’s Brenna Argall, PhD and research scientist, as recipient of a Faculty Early Career Development Program (CAREER) Award.
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The CAREER Award is the NSF’s most prestigious award in support of junior faculty. Begun in 1995, the program provides promising faculty the opportunity to pursue outstanding research, education and the integration of education and research within the context of the mission of their organizations.
Argall’s grant award totals $525,000 and will be directed toward advancing human ability through the introduction of robotics-inspired automation and adaptation to human-assistive devices.
“A significant paradox exists,” said Argall. “The more severe a person’s motor impairment, the more challenging it is for him or her to operate the very assistive machines meant to enhance their quality of life. We are spearheading a new area of research at the intersection of autonomous robots and rehabilitation, incorporating robotics autonomy and intelligence into assistive machines, offloading some of the control burden from the user to the machine.”
The proposed work contributes algorithmic approaches tailored specifically to the unique constraints of learning from motor-impaired users, and an evaluation of these algorithms by end-users.
Autonomous robots already synthetically sense the world, generate motion and compute cognition—any of which might be adapted to help address sensory, motor and cognitive impairments in humans. In order to achieve widespread adoption, Argall’s algorithm will adapt to the varied and variable needs of users. Patient populations who are poised to benefit from such advances include those with Amyotrophic Lateral Sclerosis (ALS), Muscular Dystrophy (MD), Multiple Sclerosis (MS), high level Spinal Cord Injury (SCI), Spinal Muscular Atrophy (SMA), Cerebral Palsy (CP), Traumatic Brain Injury (TBI), Parkinson Disease and stroke survivors, among others.
“We will treat motor impairments as an advantage rather than a constraint for machine leaning algorithms, enabling the customization of the machines’ control by the end-users themselves,” said Argall. “RIC—touting a diverse population of patients, clinicians and researchers—is uniquely poised to transform rehabilitation science with this work.”
