CaNRun: Non-Contact, Acoustic-based Cadence Estimation on Treadmills using Smartphones

CaNRun: Non-Contact, Acoustic-based Cadence Estimation on Treadmills using Smartphones
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CaNRun:使用智能手机对跑步机进行非接触式、基于声学的步频估计

DOI:
10.1145/3576914.3589561
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发表时间:
2023
期刊:
CPS-IoT Week '23: Proceedings of Cyber-Physical Systems and Internet of Things Week 2023
影响因子:
--
通讯作者:
Jiang, Xiaofan
Jiang, Xiaofan
中科院分区:
--
文献类型:
--
作者:
Xuan, Ziyi;Liu, Ming;Nie, Jingping;Zhao, Minghui;Xia, Stephen;Jiang, Xiaofan

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以一致的节奏(每分钟步数)跑步对跑步者来说很重要,有助于降低受伤风险,改善跑步形式,提高整体生物力学效率。我们介绍CaNRun,一个非接触和基于声学的系统,使用从放置在跑步机上的移动终端捕获的声音来预测和报告跑步节奏。CaNRun避免了跑步者在跑步机上跑步时使用可穿戴设备或在身体上携带移动终端的需要。CaNRun利用长短期记忆(LSTM)网络来提取从麦克风中观察到的步骤,以稳健地估计节奏。通过一项8人研究,我们证明了CaNRun在没有对个人用户进行校准的情况下实现了节奏检测的准确性,尽管是非接触式的,但这与Apple Watch的准确性相当。
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.
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