| NSF Org: |
TI Translational Impacts |
| Recipient: |
|
| Initial Amendment Date: | June 1, 2023 |
| Latest Amendment Date: | June 1, 2023 |
| Award Number: | 2226174 |
| Award Instrument: | Cooperative Agreement |
| Program Manager: |
Alastair Monk
amonk@nsf.gov (703)292-4392 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | June 15, 2023 |
| End Date: | May 31, 2025 (Estimated) |
| Total Intended Award Amount: | $997,735.00 |
| Total Awarded Amount to Date: | $997,735.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
23 CHERRY TREE LANE WARREN NJ US 07059-2600 (908)577-4711 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
211 Warren Street Newark NJ US 07103-3568 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | SBIR Phase II |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to potentially improve the quality of life for individuals suffering arm and hand impairments from stroke, through a medical device for telerehabilitation. Each year, ~800,000 people have a stroke in the United States, and about 65% of them suffer long-term upper extremity impairments. Due to many barriers such as cost, transportation, and time, many individuals do not obtain enough therapy for recovery. The telerehabilitation approach may reduce some of these barriers, allowing therapists and their patients to have meaningful remote sessions. For therapists, this may improve fiscal outcomes by automating the flow of reviewing patient progress, adjusting their rehabilitation treatments, and billing for services.
This project will advance the development of a personalized telerehabilitation system, specifically for hand and arm motor recovery, for individuals suffering from a stroke. New exergames designed for rehabilitation of the fingers, hand, and arm will be developed and added to the current library of games. Machine learning will be added to the system to create a versatile, engaging, and customizable solution. This novel approach to rehabilitation will personalize treatments that may be more effective by addressing individual user needs with predictive analytics. Machine learning will drive the recommendation system to synchronize the rehabilitation plan with the patient recovery trajectory. This synchronization will help the therapist provide personalized therapeutic exercises and possibly increase their patients? recovery outcomes. The games and machine learning algorithms will be evaluated with clinicians and individuals with stroke. The final step will be to test the feasibility of the system in a comprehensive stroke center. These capabilities of personalized virtual rehabilitation, remote clinician supervision, and progress tracking may offer a cost-effective way to improve patient outcomes.
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.
