| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | August 13, 2019 |
| Latest Amendment Date: | August 13, 2019 |
| Award Number: | 1935500 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Alex Leonessa
aleoness@nsf.gov (703)292-2633 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2019 |
| End Date: | August 31, 2023 (Estimated) |
| Total Intended Award Amount: | $450,000.00 |
| Total Awarded Amount to Date: | $450,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
9500 GILMAN DR LA JOLLA CA US 92093-0021 (858)534-4896 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
9500 Gilman Drive 0404 La Jolla CA US 92093-0404 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | M3X - Mind, Machine, and Motor |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
The overall research objective of this collaborative project is to create an embodied, intelligent robotic system that can induce meaningful long-term change in human motor function by providing personalized, adaptive feedback and noninvasive neural stimulation designed to induce desirable neuromotor plasticity. The motor behavior targeted for enhancement is plantarflexor power during the push-off phase of gait; stroke survivors often produce diminished plantarflexor power and rely instead on an inappropriate hip flexion "pull-off" compensation, thereby limiting the quality of their gait and quality of life. Personalized learning methods will be employed to model and optimize behavioral responses to changes in performance feedback provided by an intelligent mobile robotic coach, which will guide gait training. The project will lay the foundation to determine whether training based solely on principles of motor learning suffice to induce meaningful increases in plantarflexor power that are retained over time, or whether simultaneous targeted changes in brain excitability are required. This project advances the NSF mission to promote the progress of science and advance the national health by developing an adaptive motor learning algorithm embedded within an interactive mobile robot to induce meaningful long-term changes in human motor function through human-robot interaction. Broader impacts of the project include efforts to enhance research reproducibility and rigor, and to broaden participation in STEM for women, minorities, and persons with disabilities.
The overall objective of this research is to create an embodied, intelligent system that provides personalized, adaptive feedback to induce neuromotor plasticity, mediate motor adaptation, and promote meaningful, lasting increases in plantarflexor power, which is diminished during walking in many stroke survivors. Three sets of human subject experiments are researched. The first will identify critical parameters of performance feedback that facilitate the desired behavioral change. The second will use a novel learning paradigm to model and optimize behavioral responses to changes in performance feedback provided by an intelligent robotic coach. The third will use single-pulse transcranial magnetic stimulation (TMS) and paired associative stimulation (PAS) to harness neuroplastic effects in humans such that desired behavioral changes induced by optimized feedback training are made persistent through Hebbian learning mechanisms. The envisioned system will involve bi-directional learning between the human and machine intelligences to determine how to control important, but subject-specific, variables critical for maintaining and promoting motor function across the life and health span. Understanding these bi-directional relationships within the context of neurorehabilitation may provide insights that can further advance human-robot teaming in a range of application domains, including healthcare, manufacturing, and personal transportation.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Technology to support brain plasticity and recovery of function following a nervous system injury (e.g., stroke) needs to be personalized and account for individual differences to facilitate effective behavioral change. However, there are few systems which can account for individual differences in how people learn or how plastic their brains are, nor can support adapting and learning to people over time. For example, in neurorehabilitation, there is currently limited evidence that rehabilitation produces meaningful or persistent changes in walking function for individuals following stroke. This suggests a significant knowledge gap regarding the capacity for motor adaptation and represent an urgent unmet need obstructing development of intelligent systems designed to promote recovery of function in persons post-stroke. Thus, the objective of this project was to create an embodied, intelligent robotic system that provides personalized, adaptive feedback to induce neuromotor plasticity, mediate motor adaptation, and leverage meaningful, lasting changes in motor function.
This project engaged in several research efforts to help further this objective. First, we developed new methods for gait rehabilitation, as supported by a new bespoke robot we built called GARRY (Gait Rehabilitation Robotic System) that provides real time, interactive feedback during locomotor training. GARRY utilizes different feedback types and robotic assistance to support users' gait rehabilitation outcomes. GARRY promotes engagement by gamifying the rehabilitation process, offering a fun means for a person to meet their rehabilitation goals. GARRY also incorporates behavioral feedback to introduce a sense of companionship during a session. We made GARRY open-source to other researchers in hopes of encouraging accessibility and to promote research in the field.
We also developed an interactive learning system to support personalized feedback and adaptation. We aim for our system to learn what types of feedback people respond best to, and dynamically modify its interactions to support their performance throughout the intervention. In collaboration with clinical experts, this system was realized via an autonomous robotic system we built called CARMEN (Cognitively Assistive Robot for Motivation and Neurorehabilitation). CARMEN is a robot that learns a person's preferences and abilities, and adapts its behavior to help people achieve their intervention goals. We identified concrete strategies a robot can use to support a person's motivation over time, including helping people to identify their own intervention goals, using visual aids to highlight high performance or perseverance, or expressing empathy or excitement to augment the feedback mechanisms.
Our research on personalized robotic systems furthers the state of the art and state of the science in neurorehabilitation, and can have applications in other areas such as education, training, and other assistive technologies. To date, our research has been broadly disseminated in publications, presentations, and publicly available software via the PIs' respective websites. The PIs also engaged in activities to broaden participation in computing through research, teaching, and service activities related to the project.
Last Modified: 01/20/2024
Modified by: Laurel D Riek
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