10157783Analysis of Passive Parameters in a Smart InsoleClosedGrant for R&DInnovate UKWalk With Path Limited, a healthtech start-up focussing on enhancing mobility, has developed and validated its latest product, Path Feel. Path Feel consists of a pair of smart insoles which track foot pressure and balance, and a Bluetooth-linked mobile app. Path Feel can be used in a range of settings from at home, to monitor and prevent balance deterioration in older individuals, to clinical trials, where remotely monitoring patients can massively reduce costs and stress. With 1 in 3 people \>65 having a fall every year and falls costing the NHS £435M/year and the lack of accessible, rigorous gait-related clinical trial end-points leaves a major market gap in clinical trials, Path Feel has major potential. While smartwatches can be used to track some gait metrics, to accurately calculate falls risk, detailed data on foot pressure (not trackable by smartwatches) is required. Path Feel monitors active (app-guided clinically validated gait tests) and passive (daily movement) parameters via embedded IMUs and pressure sensors. While active parameters provide valuable information on a person's balance, they only show a snapshot of users' balance and take time to complete. Passive parameters track detailed information on day-to-day gait variability using many more data points, without requiring any active user effort. Path Feel's ability to track active parameters has been optimised and clinically validated. However, analysing passive parameters presents additional challenges due to the heterogeneity of readings taken during everyday activities. Thus, Newton Gateway to Mathematics will team up with Walk With Path to identify and filter out the sources of "noise" affecting parameter accuracy while allowing the insoles to accurately identify what activity is being carried out. Enhancing passive parameters will set Path Feel apart from snapshot balance assessment tools, like smart balance scales. It would enable: * Continuous balance monitoring without user effort * Detection of subtle gait changes indicating fall risk * Personalised insights on balance fluctuations * More comprehensive data for clinical decision-making Previous attempts to address these challenges through in-house data analysis and algorithm development have not produced reliable, validated passive metrics. Expertise from groups like the Newton Gateway to Mathematics in advanced data analysis techniques will unlock Path Feel's full potential. Enhancing Path Feel's passive parameter accuracy will increase its competitiveness in the mobility aid market by massively enhancing its usability. Meanwhile, successfully identifying daily activities and accurately monitor gait will make Path Feel a leader in the clinical trials gait monitoring market.