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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of California - Merced |
| Country | United States |
| Start Date | Dec 01, 2024 |
| End Date | Nov 30, 2025 |
| Duration | 364 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2435212 |
The broader impact/commercial potential of this I-Corps project is based on the development of a wearable muscle-sensing solution to directly measure fascial thickening when muscles are strained over long periods of time. This project prioritizes affordability, wearability, and in-home continuous monitoring, making this novel technology widely accessible to the general public.
The data collected through this platform solution can be shared with physical therapists and ergonomists to give them a holistic view of patient muscle data, thus helping them better serve patients. Muscles are highly individual, and this solution could ensure that data collection thoroughly includes underrepresented groups to mitigate bias. Furthermore, the affordability of the proposed solution will make muscle health solutions widely available to people often subject to healthcare inequities.
By helping users take the initiative to prevent chronic musculoskeletal pain, this solution could save users the $32B spent annually on pain management procedures and medications.
This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of the proposed technology. It is based on the prior development of an artificial intelligence (AI) powered wearable sensor system that utilizes haptic feedback to directly measure fascial thickening when muscles are strained over long periods of time.
Current muscle stiffness sensor solutions such as electromyography, shear wave elastography, and magnetic resonance elastography are either largely focused on research applications or are bulky and expensive, which limits their usage in clinicians’ offices. A preliminary model of the device and AI algorithm have demonstrated the feasibility of this approach, and further research is being conducted to generalize the algorithm across different physiologies and develop form factors that optimize comfort.
If successful, this solution could be used to build a more comprehensive understanding of the properties of the fascia and the relationship between fascial thickening and musculoskeletal chronic pain.
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.
University of California - Merced
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