C-FMCW Based Contactless Respiration Detection Using Acoustic Signal

Abstract

Recent advances in ubiquitous sensing technologies have exploited various approaches to monitoring vital signs. One of the vital signs is human respiration which typically requires reliable monitoring with low error rate in practice. Previous works in respiration monitoring however either incur high cost or suffer from poor error rate. In this paper, we propose a Correlation based Frequency Modulated Continuous Wave method (C-FMCW) which is able to achieve high ranging resolution. Based on C-FMCW, we present the design and implementation of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. However, several technical challenges exist when applying C-FMCW to detect respiration with commodity acoustic devices. First, the sampling frequency offset between speakers and microphones if not being corrected properly would cause high ranging errors. Second, the uncertain starting time difference between the speaker and microphone varies over time. Moreover, due to multipath effect, weak periodic components due to respiration can easily be overwhelmed by strong static components in practice. To address those challenges, we 1) propose an algorithm to compensate dynamically acoustic signal and counteract the offset between speaker and microphone; 2) co-locate speaker and microphone and use the received signal without reflection (self-interference) as a reference to eliminate the starting time difference; and 3) leverage the periodicity of respiration to extract weak periodic components with autocorrelation. Extensive experimental results show that our system detects respiration in real environments with the median error lower than 0.35 breaths/min, outperforming the state-of-the-arts.

Your Smart Speaker Can "Hear" Your Heartbeat!

“…Acoustic-based sensing techniques. Acoustic signals have been widely employed for a large variety of applications, ranging from coarse-grained localization [31] [16], gait recognition [39], driver behavior monitoring [38], gesture sensing [37] to fine-grained respiration monitoring [34], finger drawing tracking [11] and lip-reading recognition [20]. Wang et al [33] utilize the influence of the airflow changes caused by breathing on the sound wave to extract the respiration information.…”

“…By analyzing the variations of signal reflected from the target, rich target information can be obtained such as the movement speed and displacement. With the latest advance of wireless sensing, researches have successfully exploited WiFi [42], RFID [10], LoRa [41], acoustic [34] and 60 GHz [28] signals to accurately monitor the finegrained respiration. However, one interesting observation is that while there are a lot of studies on respiration monitoring already, there is very little work [26] on heartbeat monitoring.…”

An Unobtrusive and Non-Contact Method for Respiratory Measurement With Respiratory Region Detecting Algorithm Based on Depth Images

“…The PSG plays an important role in the SAHS, COPD, asthma and snore diagnosis. However, these techniques are inconvenient to hold or wear the devices and need to contact to subject while measuring which might interfere the subject's normal activity [13]. Furthermore, the airflow method which was considered as the most precise way in clinical is uncomfortable to patients [14].…”

Privacy Protection for Audio Sensing Against Multi-Microphone Adversaries

“…The ubiquity of commodity devices with microphones and speakers have made audio-based sensing of human activity and health attributes feasible and attractive. Contact-free and audio-based sensing systems can sense fine-grained human gestures [12,24,42,54,60], movements [28,30], behavior [10,21,22,29], and health attributes [33,37,53,55]. Akin to sonars, these audiobased sensing systems transmit near-ultrasound signals and analyze their reflections off the human body.…”

“…-Audio-based sensing enables contact-free monitoring of the health conditions of individuals. For example, researchers have demonstrated the efficacy of audio sensing in the detection of sleep apnea (this technology has already been licensed by a sleep health-care company) [33] and the monitoring of breathing patterns [37,53,55]. A stealthy adversary can leverage the ongoing sensing to remotely collect sensitive health information about the user.…”