SBIR/STTR Research Award

SBIR Phase I:Brave Virtual Worlds Human Movement Artificial Intelligence (AI) Engine and Biofeedback Loop

Award identifier 101e0bec253772c89154986f61c85543b9b0451c591d96e4e1b7965615e69696Awarding agency National Science Foundation

Award amount

$273,915

Program and phase

SBIR · Phase I

Award year

2023

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
BRAVE VIRTUAL WORLDS, INC.
Proposal award date
2023-09-01
Contract end date
2024-08-31
UEI
Z9AEGCRJ2G28
Solicitation number
NSF 23-515
Topic code
DH

Research abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project aims to develop a biofeedback system that combines wearable sensors, real-time data capture, and immersive virtual reality visualization. This system has the potential to enhance the understanding of joint angles and movement patterns enabling in-depth analysis of human movement. The proposed technology targets a significant market opportunity in the athletic training and physical therapy industry, which is valued at $200 million. _x000D_ _x000D_ This SBIR Phase I project addresses intellectual merit through a systematic research approach. The project involves designing and refining the biofeedback system, conducting extensive data collection, and implementing advanced algorithms for real-time analysis. The project's goals include developing a user-friendly interface, optimizing sensor accuracy, and creating a seamless biofeedback system for improved training and rehabilitation. Anticipated technical results include the design and development of a machine learning layer to classify and identify key components of movements in order to create a contextual database for further analysis to determine movement efficiency and highlight movement patterns as well as the development of a corrective exercise/feedback layer using the context from the machine learning layer to generate further insights into what is causing highlighted movement patternsThis second later will incorporate corrective exercises that are suggested for improvement. Additionally, a real-time layer incorporating the first two layers into the real-time portion will be used for immediate feedback to the end-user, thus providing a closed bio-feedback loop._x000D_ _x000D_ 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.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

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