SBIR Phase I: Steadi-scores: Translating Balance Science into Clinical Action for Proactive Balance Training

The broader/commercial impact of this SBIR Phase I project is to improve balance health across the population through the development of an innovative and accessible solution for balance training and assessment. Balance declines naturally with age, often beginning around 40, and can go unnoticed until a fall highlights the issue. Falls frequently result in significant physical, emotional, and social challenges, underscoring the need for proactive tools to address balance health.

This project aims to design a portable and affordable platform that individuals of all ages could use at home or in clinics to practice and improve their balance. The platform would offer engaging exercises while providing insights on balance progress that could help individuals better understand their balance health and take steps to mitigate fall risks.

This innovation has the potential to fill a critical gap in the market by offering an accessible, cost-effective, and scalable approach to balance health. It could enhance scientific and technological understanding by leveraging new methods to measure and support balance. By addressing a widespread issue, this platform could meet a large market need in home and clinic-based healthcare, establishing a foundation for future commercial success while advancing health, mobility, and independence for a diverse population.

This Small Business Innovation Research (SBIR) Phase I project addresses the challenge of integrating biomechanical balance metrics, such as center-of-pressure and sway data, with functional clinical assessments like the Mini-BESTest, which are commonly used to evaluate balance capabilities. Clinical assessments often rely on subjective ratings that can introduce bias and may fail to capture subtle improvements in individuals with higher balance abilities.

On the other hand, biomechanical metrics provide objective, quantitative measures but lack a direct, clinically validated connection to functional outcomes. This project seeks to bridge this gap by investigating how biomechanical data from interactive balance activities can be aligned with clinically relevant measures. Using advanced machine learning techniques, the project will develop models to map biomechanical metrics to outcomes derived from the Mini-BESTest framework.

These models will enable the creation of a clinically grounded balance score that reflects patient progress with greater specificity and sensitivity. The anticipated results include a robust framework for integrating biomechanical and clinical approaches, advancing balance assessment methodologies, and enhancing the accuracy of balance evaluations through data-driven tools.

This work could provide clinicians with a scientifically validated method for tracking balance improvement and guiding interventions with greater precision.

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.