Year: 2024
Type: conference-paper
Abstract: Over half a trillion recreational runners worldwide engage in running for psychological, health, and social benefits. Running metrics are essential for motivation, goal setting, performance improvement, health management, and injury prevention. Although wearable devices like fitness trackers and smartwatches offer various metrics, they often perform poorly on treadmills and can be uncomfortable or restrictive. In this work, we propose a non-contact, real-time, smartphone-based approach to estimate running metrics, including cadence, ground contact time (GCT), and balance, using the sound produced during treadmill running. In collaboration with a licensed running coach, we recruited over 50 subjects with varying levels of running expertise. We collected treadmill running sounds and ground-truth running metrics in different environments. We designed and developed a multi-task learning (MTL) machine learning mobile system to capture the treadmill running sounds and estimate running metrics in situ. Our proposed method shows comparable accuracy in estimating running metrics to commercial off-the-shelf (COTS) wearable devices.
Institution: Columbia University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W4405014949