Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning
Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning
复制标题
通过频时深度学习进行可穿戴射频近场咳嗽监测
DOI：
10.1109/tbcas.2021.3099865
复制
发表时间：
2021
影响因子：
5.1
通讯作者：
Kan, Edwin C.
中科院分区：
文献类型：
--
作者：
Hui, Xiaonan;Zhou, Jianlin;Sharma, Pragya;Conroy, Thomas B.;Zhang, Zijing;Kan, Edwin C.
咳嗽是许多呼吸系统疾病的常见症状，可以传播含有细菌和病毒病原体的不同大小的飞沫。轻微咳嗽通常在早期阶段被忽视，不仅因为它们几乎不被人和周围的人察觉，而且因为目前的记录方法不舒服、不隐私，也不适合长期监测。本文提出了一种可穿戴式射频传感器，可以直接从局部气管振动特征中识别轻微咳嗽信号，并能隔离周围人的干扰。该传感器工作在超高频段，可以通过传感天线的近场将射频能量耦合到上呼吸道轨迹。然后，可以通过在频率-时间谱上训练的卷积神经网络来分析由咳嗽气流爆发引起的恢复的组织振动。为了提高性能，对传感天线的设计进行了分析。对5名受试者进行100分钟以上的人体实验，总识别率在90%以上，其他动作的假阳性率在2.09%以下。
Coughing is a common symptom for many respiratory disorders, and can spread droplets of various sizes containing bacterial and viral pathogens. Mild coughs are usually overlooked in the early stage, not only because they are barely noticeable by the person and the people around, but also because the present recording method is not comfortable, private, or reliable for long-term monitoring. In this paper, a wearable radio-frequency (RF) sensor is presented to recognize the mild cough signal directly from the local trachea vibration characteristics, and can isolate interferences from nearby people. The sensor operates at the ultra-high-frequency band, and can couple the RF energy to the upper respiratory track by the near field of the sensing antenna. The retrieved tissue vibration caused by the cough airflow burst can then be analyzed by a convolutional neural network trained on the frequency-time spectra. The sensing antenna design is analyzed for performance improvement. During the human study of 5 participants over 100 minutes of prescribed routines, the overall recognition ratio is above 90% and the false positive ratio during other routines is below 2.09%.