Methods to improve the use of wearable sensors in human movement analyses.

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Gait analysis research is important for analysing sport performance, pathological gait, or the effects of aging. Unfortunately, conventional optical gait analysis systems are expensive, time-consuming, and confined to laboratories, which limits their accessibility and practical application. Wearable sensors offer a cost-effective alternative to conventional systems, with the unique ability to collect data in real-world conditions. While this may be the new frontier of biomechanics research, wearable sensors have generally failed to realize their potential for gait analyses in real-world, uncontrolled settings. This disconnect between what wearable sensors are capable of and what they are currently used for is largely based on the difficulty in processing and managing the large amounts of data collected. Therefore, if wearable sensors are to fulfill their real-world potential, there is a critical need to address these foundational methodological gaps across the scientific and biomechanics community. The long-term vision of my research program is to provide the biomechanics community with new tools to help make human movement analyses more accessible. Wearable sensors provide a clear opportunity to support this vision, but there is an immediate need to improve the ways in which these data are collected, processed, interpreted, and visualized. Therefore, the short-term goals of this research will focus on i) improving activity classification and event detection algorithms for wearable sensor data, ii) developing robust wearable sensor processing pipelines, iii) evaluating the sensitivity to change in uncontrolled, real-world gait patterns, and iv) developing new wearable sensor data visualization techniques. Building on my past successes in the areas of wearable sensors and machine learning in human movement analyses, this research will make the collection of wearable sensor data in real-world, uncontrolled settings more efficient, more effective, and more interpretable. Therefore, this foundational work will develop new analytical methods to empower researchers to collect better and more representative human movement data, which will in turn enable better informed decision-making regarding healthy aging and the treatment of musculoskeletal disorders. Moreover, this research will develop my program as a world-leader in this emerging area of wearable technology and human movement, while driving future academic and industry achievements for Canada and my HQP.