Learning from Wearable technologies: Investigating running asymmetries through machine learning and inertial sensors
University of Technology Sydney
Lauren Wood is a researcher and educator exploring how wearable sensors and artificial intelligence can help us better understand human movement. With an interdisciplinary background spanning sport science, engineering education, and design, Lauren is passionate about shaping the future of sport and health technology through design thinking and data analytics.
Lauren has collaborated with elite sporting organisations as a research assistant to integrate digital tools into high-performance environments to improve usability and support holistic wellbeing from grassroots to the elite level. She also teaches engineering design and innovation at UNSW Sydney, leading student teams in human-centred design, prototyping, and technology development. Lauren aims to always take a systems approach that connects the athlete, coach, and researcher to make wearable data meaningful, ethical, and actionable.
Lauren is currently completing a PhD at the University of Technology Sydney (UTS), where she focuses on detecting subtle running gait asymmetries using wearable inertial sensors and machine-learning models. Her work brings together biomechanics, data science, and design to translate complex sensor data into practical insights for athlete wellbeing, rehabilitation, and performance.
Lauren and I came into this conversation knowing we shared a mutual interest in design, but we’re also both very interested in wearable tech. As a result, this conversation takes a few novel turns, including a discussion of how Lauren has had to teach herself coding even while undertaking her PhD – something I think will fascinate you as much as it did me.
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This episode of The Knowledge Mill was recorded on the Moore Park Campus of the University of Technology Sydney on July 17, 2025.
Show Notes
University of Technology Sydney
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