I-Corps: Artificial Intelligence-Enabled Shoe Insoles to Assess Walking Function in Real Life Environments

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The broader impact/commercial potential of this I-Corps project is the development of sensitive, quantitative assessments that focus on ambulatory function in real-life settings to evaluate safety and effectiveness of new treatments in clinical trials and post-marketing surveillance in individuals with neurological conditions, such as neuromuscular disorders, Parkinson’s disease, Huntington disease, and Multiple Sclerosis. Walking is the most frequent activity of daily living and a key contributor to functional independence. New pharmacological treatments are changing the therapeutic landscape for several neurological conditions, altering their natural history, with encouraging results in ambulatory function. Walking-related digital mobility outcomes have the potential to objectively capture daily performance to supplement clinical assessments and patient-reported outcomes, thereby providing a more comprehensive picture of a patient’s condition. Interest in the use of wearables in clinical research to assess the efficacy of new interventions is on the rise. It is estimated that by 2025 nearly 70% of all clinical trials will involve wearables, while the average pharma company can save $100 million/year in trial development spending by adopting more objective, sensitive, and granular digital mobility outcomes. Yet, most devices for real-life gait monitoring are limited to volume digital mobility outcomes, which are easy to capture but lack stride-by-stride granularity. This innovation could enable the collection of accurate volume and stride-by-stride gait metrics longitudinally, in patients’ living environments, to help understand how disease trajectories are affected by new treatments. By capturing subtle but clinically meaningful functional changes over shorter time periods, the innovation could reduce the cost of clinical trials and bring effective treatments to patients faster and more affordably.This I-Corps project is based on the development of abstraction models that synergistically combine conventional signal processing methods for wearable gait monitoring systems with the vast expressive capability of machine learning regression and the superior personalization properties of transductive inference. Instead of replacing conventional methods, this innovation's machine learning models correct their outputs using an optimized set of input features, thereby generating accurate stride-by-stride spatiotemporal and kinetic digital mobility outcomes without the computational burden that plagues end-to-end machine learning models and hinders their implementation in embedded systems. By leveraging the strengths of transductive inference, the innovation's models provide unprecedented accuracy over extended-time measurements without the need for subject-specific labelled data. This novel approach provides algorithmic support for next-generation wearable technology to fill the accuracy gap between gold-standard laboratory equipment and emerging wearable gait monitoring devices, which hampers the widespread use of these systems in clinical research. By widening the range of applicability of wearable gait monitoring devices, the innovation will promote the understanding of real-life ambulatory function and its trajectories over time in healthy and clinical populations.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.