Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2024
Abstract
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.
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Areas of Excellence
Digital transformation
Publication
BodySYS '24: Proceedings of the 10th Workshop on Body-Centric Computing Systems, Tokyo, June 3-7
First Page
35
Last Page
40
ISBN
9798400706660
Identifier
10.1145/3662009.3662023
Publisher
ACM
City or Country
New York
Citation
HU, Changshuo; KANDAPPU, Thivya; STUCHBURY-WASS, Jake; LIU, Yang; TANG, Anthony; MASCOLO, Cecelia; and MA, Dong.
Detecting foot strikes during running with earbuds. (2024). BodySYS '24: Proceedings of the 10th Workshop on Body-Centric Computing Systems, Tokyo, June 3-7. 35-40.
Available at: https://ink.library.smu.edu.sg/sis_research/9040
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3662009.3662023