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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Texas At Austin |
| Country | United States |
| Start Date | Dec 01, 2024 |
| End Date | Nov 30, 2026 |
| Duration | 729 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2436152 |
The broader impact/commercial potential of this I-Corps project is the development of a wearable technology for dysphagia monitoring. Dysphagia is a healthcare condition characterized by difficulty in swallowing food or liquid. Current dysphagia management involves invasive, uncomfortable, and costly procedures, reaching only a fraction of the 9.5 million affected individuals in the U.S. annually.
The proposed technology is a knitted fabric sensing collar offering non-invasive, continuous monitoring with significantly increased diagnostic precision. Its comfort and usability make it suitable for continuous wear, enabling early detection and management of swallowing disorders. In addition, this technology facilitates early intervention and reduces clinical session times by 25%, and integrates into telehealth frameworks.
The device may be extended to potential applications in other muscle monitoring needs, paving the way for broader diagnostic and therapeutic uses. The goal is to integrate advanced wearable technology into routine medical practice, enhancing patient quality of life and fostering more efficient healthcare delivery systems.
This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of an advanced sensing knitted wearable technology designed for continuous, real-time monitoring and classification of swallowing actions. This device integrates three knitted strain sensors into a low-profile, ergonomic collar, enabling seamless wear with everyday attire.
The sensors accurately measure laryngeal elevation, crucial for dysphagia management and rehabilitation. Bluetooth technology transmits data to a graphical user interface (GUI), providing immediate clinical feedback for remote monitoring and timely intervention. In addition, the device incorporates advanced signal processing and machine learning models, enhancing swallowing classification accuracy by 15-50% over traditional surface electromyography (sEMG).
The proposed technology offers a non-invasive, efficient alternative to conventional dysphagia management with up to 12 hours of continuous monitoring per battery charge, reducing the frequency of clinical visits. This wearable sensing technology may offer a promising solution to enhance patient outcomes and facilitate proactive healthcare monitoring.
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 Texas At Austin
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