Coughwatch:使用智能手表进行真实咳嗽检测

D. Liaqat, S. Liaqat, Jun Lin Chen, Tina Sedaghat, Moshe Gabel, Frank Rudzicz, E. D. Lara
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引用次数: 15

摘要

对咳嗽的持续监测可以提供对个人健康状况以及治疗效果的深入了解。尤其是智能手表,在这种监测方面非常有前途:它们价格低廉、不显眼、可编程,而且有各种各样的传感器。然而,目前的移动咳嗽检测系统并不是为智能手表设计的,并且在应用于现实世界的智能手表数据时表现不佳,因为它们通常是根据实验室收集的数据进行评估的。在这项工作中,我们提出了CoughWatch,一个轻量级的咳嗽检测器,用于智能手表,使用音频和运动数据进行野外咳嗽检测。在我们的野外数据中,CoughWatch的准确率为82%,召回率为55%,而目前最先进的方法的准确率为6%,召回率为19%。此外,通过整合陀螺仪和加速度计数据,与纯音频模型相比,CoughWatch的精度提高了15.5个百分点。
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Coughwatch: Real-World Cough Detection using Smartwatches
Continuous monitoring of cough may provide insights into the health of individuals as well as the effectiveness of treatments. Smart-watches, in particular, are highly promising for such monitoring: they are inexpensive, unobtrusive, programmable, and have a variety of sensors. However, current mobile cough detection systems are not designed for smartwatches, and perform poorly when applied to real-world smartwatch data since they are often evaluated on data collected in the lab.In this work we propose CoughWatch, a lightweight cough detector for smartwatches that uses audio and movement data for in-the-wild cough detection. On our in-the-wild data, CoughWatch achieves a precision of 82% and recall of 55%, compared to 6% precision and 19% recall achieved by the current state-of-the-art approach. Furthermore, by incorporating gyroscope and accelerometer data, CoughWatch improves precision by up to 15.5 percentage points compared to an audio-only model.
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