SonarBeat: Sonar Phase for Breathing Beat Monitoring with Smartphones

Wang, Xuyu; Huang, Runze; Mao, Shiwen

doi:10.1109/icccn.2017.8038412

Cited by 11 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with Wang et al's work [21], we get similar results in estimation error with a much simpler algorithm. They employed sonar phase data to get the breath rate, resulting in high complexity in algorithm.…”

Section: System Evaluationsupporting

confidence: 68%

“…Most off-the-shelf smartphones can generate sound up to 22 kHz using their built-in speakers [20, 21]. The smartphone is placed in front of the tester.…”

Section: Ultrasonic Signal Analysismentioning

confidence: 99%

“…That is because the frequency domain of music is relatively higher than people's voice; therefore, music has a higher influence on the 20 kHz signal that we use to monitor the breath. We did not test our system under the scenario when the tester is running, because all existing works [17, 21] conduct their experiments in a stable scenario. The experiments validate that, even in noisy scenarios, we can get mean estimation errors less than 1 bpm.…”

Section: System Evaluationmentioning

confidence: 99%

“…Instead of specially deployed wireless transceivers, some researchers proposed using smartphones to detect vital signs, which is easier to access in daily life. Some works used built-in inertial sensors in smartphones to monitor vital signs [16–19], while others leveraged ultrasonic signals to conduct respiration detection and sleep monitoring [20, 21]; they utilized built-in speakers and microphones in smartphones to play and record ultrasound signals and extract useful information such as respiration patterns from them [22]. In our paper, we will also use ultrasound as the media to detect the respiration rate.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Zhang

Wei

2018

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Respiration monitoring is helpful in disease prevention and diagnosis. Traditional respiration monitoring requires users to wear devices on their bodies, which is inconvenient for them. In this paper, we aim to design a noncontact respiration rate detection system utilizing off-the-shelf smartphones. We utilize the single-frequency ultrasound as the media to detect the respiration activity. By analyzing the ultrasound signals received by the built-in microphone sensor in a smartphone, our system can derive the respiration rate of the user. The advantage of our method is that the transmitted signal is easy to generate and the signal analysis is simple, which has lower power consumption and thus is suitable for long-term monitoring in daily life. The experimental result shows that our system can achieve accurate respiration rate estimation under various scenarios.

show abstract

Section: System Evaluationsupporting

confidence: 68%

“…Most off-the-shelf smartphones can generate sound up to 22 kHz using their built-in speakers [20, 21]. The smartphone is placed in front of the tester.…”

Section: Ultrasonic Signal Analysismentioning

confidence: 99%

Section: System Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Zhang

Wei

2018

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection for Audio Sensing Against Multi-Microphone Adversaries

Gao

Fawaz

Sur

et al. 2019

Proceedings on Privacy Enhancing Technologies

View full text Add to dashboard Cite

Audio-based sensing enables fine-grained human activity detection, such as sensing hand gestures and contact-free estimation of the breathing rate. A passive adversary, equipped with microphones, can leverage the ongoing sensing to infer private information about individuals. Further, with multiple microphones, a beamforming-capable adversary can defeat the previously-proposed privacy protection obfuscation techniques. Such an adversary can isolate the obfuscation signal and cancel it, even when situated behind a wall. AudioSentry is the first to address the privacy problem in audio sensing by protecting the users against a multi-microphone adversary. It utilizes the commodity and audio-capable devices, already available in the user's environment, to form a distributed obfuscator array. AudioSentry packs a novel technique to carefully generate obfuscation beams in different directions, preventing the multi-microphone adversary from canceling the obfuscation signal. AudioSentry follows by a dynamic channel estimation scheme to preserve authorized sensing under obfuscation. AudioSentry offers the advantages of being practical to deploy and effective against an adversary with a large number of microphones. Our extensive evaluations with commodity devices show that AudioSentry protects the user's privacy against a 16microphone adversary with only four commodity obfuscators, regardless of the adversary's position. AudioSentry provides its privacy-preserving features with little overhead on the authorized sensor.

show abstract

Acceptability of Technological Solutions for Overdose Monitoring: Perspectives of People who use Drugs

et al. 2021

View full text Add to dashboard Cite

Introduction Rising overdose deaths are a devastating problem across the United States and Canada. Despite the availability of observed consumption sites in British Columbia (BC), data suggest people who use drugs (PWUD) alone in private residences are most at risk of overdose death. These individuals may not use consumption sites due to fear of stigmatization, lack of privacy, or personal preference. It is this population that would benefit from overdose monitoring alternatives. Methods This 2018 study employed two sources of data. (1) A provincial harm reduction distribution site client survey administered at 27 sites across BC asked about cell phone possession and functionality. (2) Structured interviews with PWUD in Vancouver gathered perspectives regarding monitoring devices and alerting systems. Results The client survey was administered to 486 individuals. Among 443 respondents, 48% ( n = 212) owned a cellphone and 68% ( n = 115) of individuals with a cellphone with access to internet ( n = 168) would use an application to mitigate opioid-related overdose deaths. Thirty qualitative interviews were performed; thematic analysis of the responses identified three major themes – safety, public versus private drug use, and technological monitoring and function. The relevance of technological devices was affected by the inconsistent availability of cellphones, concerns about anonymity, as well as personal comfort while using drugs. Conclusions Technological applications may not be suitable for clients with transient lifestyles, no permanent home, and lack of consistent access to a mobile device. Thorough needs assessments, considerations of lifestyle limitations, and understanding perceptions around cellphone and overdose monitoring can increase the potential that devices will be used. Highlights • 45% ( n = 219) of client survey respondents owned a cellphone • Out of cellphone owners, 59% ( n = 102) of client survey respondents had access to internet • Of 168 with internet access, 68% willing to use any applications for overdose response • Applications not suitable for transient lifestyles, no permanent home, and lack of consistent access to cellphone

show abstract

SonarBeat: Sonar Phase for Breathing Beat Monitoring with Smartphones

Cited by 11 publications

References 15 publications

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Single-Frequency Ultrasound-Based Respiration Rate Estimation with Smartphones

Privacy Protection for Audio Sensing Against Multi-Microphone Adversaries

Acceptability of Technological Solutions for Overdose Monitoring: Perspectives of People who use Drugs

Contact Info

Product

Resources

About