Noncontact Respiration Detection Leveraging Music and Broadcast Signals
Noncontact Respiration Detection Leveraging Music and Broadcast Signals
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DOI：
10.1109/jiot.2020.3021915
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发表时间：
2021-02
影响因子：
10.6
通讯作者：
Wentao Xie;Runxin Tian;Jin Zhang;Qian Zhang
中科院分区：
文献类型：
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作者：
Wentao Xie;Runxin Tian;Jin Zhang;Qian Zhang
最近的工作已经表明，可以利用声学信号来执行具有高精度和低能耗的呼吸监测。由于智能手机、智能扬声器和许多其他物联网设备已经配备了麦克风和扬声器，因此可以方便地在这些设备上实现声学传感解决方案。然而，现有技术需要扬声器发射某些超声波信号来检测呼吸。虽然成年人听不到这些信号，但儿童和宠物可以听到，甚至可能对植物产生负面影响。在这篇文章中，我们试图利用日常生活中的听觉信号，而不是使用超声波信号，例如，音乐或广播音频，以检测人体呼吸。我们设计了一个呼吸检测系统，通过使用音乐和广播信号连续估计信道脉冲响应（CIR）来获得呼吸率。研究了音乐和广播信号的随机性带来的码间干扰，并给出了减小干扰的策略。我们也提出了一些技术来解决一些实际问题，如多路径效应和扬声器和麦克风之间的采样频率偏移。大量的实验证明了我们的系统的可行性。实验结果表明，当使用不同的音频信号时，该系统可以达到较高的呼吸检测精度，平均误差小于0.5 BPM。
Recent works have shown that acoustic signals can be leveraged to perform respiration monitoring with high accuracy and low energy consumption. Since smartphones, smart speakers, and many other IoT devices are already equipped with microphones and speakers, it is convenient to implement the acoustic sensing solutions on those devices. However, the existing technologies require the speaker to transmit certain ultrasonic signals to detect respiration. Although these signals are inaudible to adults, they are audible to children and pets and they may even have negative impacts on plants. In this article, instead of using ultrasonic signals, we are trying to leverage audible signals in daily lives, e.g., music or broadcasting audios, to detect human respiration. We design a respiration detection system which derives the respiration rate by continuously estimates the channel impulse response (CIR) using music and broadcast signals. We study the intersymbol interference (ISI) brought by the randomness of music and broadcast signal and give our strategy to minimize the interference. We also propose several techniques to resolve some practical issues, such as the multipath effect and sampling frequency offset between the speaker and the microphone. Extensive experiments are conducted to demonstrate the feasibility of our system. The result shows that our system can achieve high respiration detection accuracy with the mean error of less than 0.5 BPM when different audio signals are used.