Contactless Respiration Monitoring Using Ultrasound Signal With Off-the-Shelf Audio Devices
Contactless Respiration Monitoring Using Ultrasound Signal With Off-the-Shelf Audio Devices
复制标题
使用声学信号和现成音频设备进行非接触式呼吸监测
DOI：
10.1109/jiot.2018.2877607
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发表时间：
2019-04-01
影响因子：
10.6
通讯作者：
Zhou, Xingshe
中科院分区：
文献类型：
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作者：
Wang, Tianben;Zhang, Daqing;Zhou, Xingshe
近年来，物联网技术及其应用取得了进步，使智能家居中的非接触式传感和老年人护理成为可能。连续、实时的呼吸监测是促进老年人睡眠辅助生活的重要应用之一，引起了学术界和工业界的广泛关注。大多数现有的呼吸监测系统需要昂贵的专门设备来感知胸部移位。然而，胸部移位不是呼吸的直接指标，因此经常会发生错误检测。在本文中，我们设计并实现了一个实时非接触式的呼吸监测系统，通过使用现成的扬声器和麦克风，利用超声信号直接感知呼吸中呼出的气流。从呼吸中呼出的气流可以看作是空气湍流，它散射声波，产生多普勒效应。该系统作为声波雷达发射声波，探测呼吸气流产生的多普勒效应。我们从数学上模拟了多普勒频率变化与呼吸气流方向的关系。在该模型的基础上，设计了一种基于最小描述长度的算法来有效捕捉呼出气流引起的多普勒效应。我们在四个不同的房间里对25名参与者(7名老年人，2名幼儿和16名成年人，包括11名女性和14名男性)进行了广泛的实验。参与者在床的不同位置采取四种不同的睡眠姿势(仰卧、左右侧卧和仰卧)。实验结果表明，该系统对呼吸监测的中位误差小于0.3次/分钟(2%)，并能准确识别呼吸暂停。实验结果还表明，该系统对不同的呼吸方式(浅呼吸、正常呼吸和深呼吸)、呼吸频率变化、环境噪声、感知距离变化(0.7m以内)和传输信号频率变化具有较强的鲁棒性。
Recent years have witnessed advances of Internet of Things technologies and their applications to enable contactless sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one’s back, on right/left side, and on one’s stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation.