The University of New Brunswick

Published purpose

This project explores operational integration for a novel balance and mobility assessment technology aimed at fall prevention and aging in place. Using pressure-sensitive tiles and computer vision with predictive algorithms, the system generates objective, interpretable mobility metrics in community settings. It addresses limitations of current assessments, which are often reactive, subjective, or restricted to specialized labs. The research evaluates these tools and potential implementation pathways such as physiotherapy clinics, senior centers, and long-term care facilities. Grounded in translational research, the project bridges lab-based innovation with real-world application, emphasizing scalable, privacy-conscious solutions that integrate into existing care workflows. The goal is to leverage machine learning-enabled technology to improve awareness, early detection, and management of functional decline, support caregivers and clinicians, and empower older adults to monitor their mobility and wellness. Findings will inform strategies for broader adoption of digital health tools and commercialization, contributing to sustainable, community-based aging solutions that enhance independence and quality of life.