Detecting foot strikes during running with earbuds

• Main Authors: HU, Changshuo, KANDAPPU, Thivya, STUCHBURY-WASS, Jake, LIU, Yang, TANG, Anthony, MASCOLO, Cecelia, MA, Dong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9040; https://ink.library.smu.edu.sg/context/sis_research/article/10043/viewcontent/3662009.3662023_pvoa_cc_by.pdf
• Institution: Singapore Management University

Running is a widely embraced form of aerobic exercise, offering various physical and mental benefits. However, improper running gaits (i.e., the way of foot landing) can pose safety risks and impact running efficiency. As many runners lack the knowledge or continuous attention to manage their foot strikes during running, in this work, we present a portable and non-invasive running gait monitoring system. Specifically, we leverage the in-ear microphone on wireless earbuds to capture the vibrations generated by foot strikes. Landing with different parts of the foot (e.g., forefoot and heel) generates distinct vibration patterns, and thus we utilize machine learning to classify these patterns for running gait detection. With data collected from 25 subjects, our system achieves an accuracy of 87.80% in identifying three gait types. We also demonstrate its robustness under a variety of scenarios and measure its system performance.