Abstract
Running with a consistent cadence (number of steps per minute) is important for runners to help reduce risk of injury, improve running form, and enhance overall bio-mechanical efficiency. We introduce CaNRun, a non-contact and acoustic-based system that uses sound captured from a mobile device placed on a treadmill to predict and report running cadence. CaNRun obviates the need for runners to utilize wearable devices or carry a mobile device on their body while running on a treadmill. CaNRun leverages a long short-term memory (LSTM) network to extract steps observed from the microphone to robustly estimate cadence. Through an 8-person study, we demonstrate that CaNRun achieves cadence detection accuracy without calibration for individual users, which is comparable to the accuracy of the Apple Watch despite being non-contact.
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.