UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator

By Heba Abdelnasser, Khaled A. Harras, Moustafa Youssef. ACM Interational Symposium on Mobile Ad Hoc Networking and Computing, 2015

301 citations recorded by the source corpus.

Monitoring breathing rates and patterns helps in the diagnosis and potential avoidance of various health problems. Current solutions for respiratory monitoring, however, are usually invasive and/or limited to medical facilities. In this paper, we propose a novel respiratory monitoring system, UbiBreathe, based on ubiquitous off-the-shelf WiFi-enabled devices. Our experiments show that the received signal strength (RSS) at a WiFi-enabled device held on a person's chest is affected by the breathing process. This effect extends to scenarios when the person is situated on the line-of-sight (LOS) between the access point and the device, even without holding it. UbiBreathe leverages these changes in the WiFi RSS patterns to enable ubiquitous non-invasive respiratory rate estimation, as well as apnea detection. We propose the full architecture and design for UbiBreathe, incorporating various modules that help reliably extract the hidden breathing signal from a noisy WiFi RSS. The system handles various challenges such as noise elimination, interfering humans, sudden user movements, as well as detecting abnormal breathing situations. Our implementation of UbiBreathe using off-the-shelf devices in a wide range of environmental conditions shows that it can estimate different breathing rates with less than 1 breaths per minute (bpm) error. In addition, UbiBreathe can detect apnea with more than 96% accuracy in both the device-on-chest and hands-free scenarios. This highlights its suitability for a new class of anywhere respiratory monitoring.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1041d7c5cf2e569ba2a913fbc9374fffc9244506

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Shiwen Mao
2. Gang Zhou
3. Xuyu Wang
4. Hongbo Liu
5. Chao Yang
6. Jian Liu
7. Yongsen Ma
8. Yan Wang

In Electrical and Electronic Engineering

1. Shiwen Mao
2. Xuyu Wang
3. Gang Zhou
4. Tolga Soyata
5. Chen Wang
6. Hongbo Liu
7. Chao Yang
8. Jian Liu

Citations in the corpus

• WiFi Sensing with Channel State Information (2019, background): “UbiBreathe [1] NR: Local Mean Removal, α -Trimmed Mean Filter; ST: FFT, DWT; SE: BPF, Thresholding M: dominant periodic component due to inhaling and exhaling Breathing Rate & Apnea Estimation breath rate error: 1bpm; breath apnea accuracy: 96%”
• A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “ing toward the measurement of multiple biomarkers, such as respiration [31], [32], heartbeat [33], and motion detection [34], [35], although practical designs are still an active”
• Wireless Sensing for Human Activity: A Survey (2020, background): “Additionally, UbiBreath [48] can achieve accurate estimation of user’s breathing rate with the error less than 1 breaths per minute (bpm) and also detect apnea with more than 96% accuracy.”
• PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices (2017, methodology): “For WiFi based vital signs monitoring, UbiBreathe uses WiFi RSS for breathing rate estimation, which requires the device be placed along the LOS path between the transmitter and the receiver for monitoring the breathing signal [10].”