SoundTrack: A Contactless Mobile Solution for Real-time Running Metric Estimation for Treadmill Running in the Wild

Abstract

Running metrics like cadence and ground contact time (GCT) are crucial for both novice and experienced runners to optimize performance and prevent injuries. We present SoundTrack, a contactless mobile solution that estimates these metrics by analyzing treadmill running sounds using on-device machine learning. Our main contributions are - (i) SoundTrackDB - a comprehensive 40-hour dataset of treadmill running sounds collected from 61 subjects across 363 sessions in 13 public gyms, created in collaboration with a licensed running coach; and (ii) SoundTrack: an on-device mobile system capturing treadmill running sounds, mitigating noise, estimating cadence and GCT with a custom multi-layer perceptron (MLP) model, and providing real-time feedback. Microbenchmarks and evaluations show that SoundTrack effectively mitigates real-world noise challenges in public gyms and adapts to individual variations among runners and treadmill models. It achieves mean absolute percentage errors (MAPEs) of 1.62% for cadence and 6.05% for GCT on the test set of unseen running sessions, yielding results that are superior or comparable to commercial sports wearables. SoundTrack offers an accessible solution for treadmill metrics on mobile platforms, reducing reliance on specialized wearables and broadening accessibility.

Minghui (Scott) Zhao

Ph.D. Candidate in Electrical Engineering

My research focuses on developing embodied and embedded AI systems that enable intelligent agents to perceive, understand, and act in the physical world through hardware-software co-design and physics-informed machine learning.