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Title: CaNRun: Non-Contact, Acoustic-based Cadence Estimation on Treadmills using Smartphones
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.  more » « less
Award ID(s):
1704899
PAR ID:
10416036
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
CPS-IoT Week '23: Proceedings of Cyber-Physical Systems and Internet of Things Week 2023
Page Range / eLocation ID:
272 to 277
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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