Inaugural awards advance automated technologies created to tackle stroke, product assembly and mental health and wellness
USF Research & Innovation and the Florida High Tech Corridor are partnering to provide $75,000 to support the advancement of technologies developed by USF researchers. It's part of the Early-Stage Innovation Fund, a pilot program launched in fall 2022. The first round of seed funding will support the development of three technologies with significant commercial potential:
- A first-of-its-kind, AI-based language translation tool for stroke patients with aphasia
- A prototype device to reduce structural malfunctions in critical manufactured products and infrastructure
- An AI-based tool to deliver custom music therapy to aging populations
Awarded projects are led by faculty from the USF Health Morsani College of Medicine, the College of Engineering and the College of Behavioral & Community Sciences.
"In research labs across USF, there are innovative ideas that, with an initial catalytic investment and dedicated time, can develop into something with tremendous public impact," said Sylvia Thomas, interim vice president of USF Research & Innovation. "The Early-Stage Innovation Fund is an effort to bring a number of those exceptional technologies closer to the marketplace. We are excited to track the long-term societal impacts this seed funding can have for USF faculty, our tech community and the broader economy."
"This program was designed in direct response to feedback The Corridor and USF Research & Innovation received from faculty on how we can further support USF's research community," said Paul Sohl, CEO of The Florida High Tech Corridor. "The response to this initial round has been tremendous and the creativity, inventive spirit and collaborative nature of USF faculty is astounding."
Below are the projects funded through the Early-Stage Innovation Fund supported by The Florida High Tech Corridor and USF Research & Innovation:
Personalized aphasia communication assistant (AI-Phasia)
Dr. W. Scott Burgin, USF Health Morsani College of Medicine, Department of Neurology
Collaborators: Hana Kim, College of Behavioral & Community Sciences, Department of Communications Sciences & Disorders
Yasin Yilmaz, College of Engineering, Department of Electrical Engineering
Approximately one-third of stroke patients suffer from aphasia, a language disorder resulting in long-term challenges to communicate, which may result in social isolation. USF researchers are developing an artificially intelligent communication assistant capable of helping people with aphasia participate in coherent and customizable conversations using state-of-the-art, generative natural language processing techniques. This platform may have additional applications in the future.
Novel assembly method and tool for achieving joint stability with reduced uncertainty
Daniel Hess, College of Engineering, Department of Mechanical Engineering
The current and most common method for achieving preload in a bolted joint, the tension created when tightening a fastener, is to apply proper torque determined by previously obtained data from sample hardware. The uncertainty with this existing method is significant and can result in under and over preload conditions which results in unreliability and failures in assembled products including critical applications in spacecraft, aircraft, military equipment and bridges. Dr. Hess' past research has led to a novel method that significantly reduces this uncertainty and associated problems. The focus of the proposed project is to develop a prototype tool that integrates and automates this novel method.
Computer vision-based human affect assessment system for precision music intervention delivery: A proof-of-concept study
Dr. Hongdao Meng, College of Behavioral & Community Sciences, School of Aging Studies
Collaborators: Yu Sun, Dmitry Goldgof, Shaun Canavan, College of Engineering, Department of Computer Science and Engineering
Mingyang Li, College of Engineering, Department of Industrial and Management Systems Engineering
Music has been shown to have a powerful effect on improving mood, reducing anxiety and depression, and even alleviating pain. Despite its therapeutic potential, recorded music has not been widely used for mood modulation, symptom management, and easing caregiver burden in the healthcare system. One of the key challenges is the lack of a cost-effective solution to adjust music selection and playback based on human affect feedback. To address this challenge, researchers are combining two USF-developed technologies to create a new product that can assess the listener's responses to music in real-time and then use momentary human affect to tailor music automatically. The multidisciplinary team aims to develop an integrated system and conduct a proof-of-concept study in 20 adults. This approach has the potential to improve access to precision music intervention in healthcare settings and enhance the well-being of patients and caregivers.
