Heba Abdelnasser
Moustafa Youssef
ABSTRACT
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.
- H. Abdel-Nasser et al. MonoPHY: Mono-stream-based device-free WLAN localization via physical layer information. In IEEE WCNC, 2013.Google Scholar
- H. Abdelnasser et al. WiGest: A ubiquitous WiFi-based gesture recognition system. In IEEE INFOCOM, 2015.Google ScholarCross Ref
- A. Al-Husseiny and M. Youssef. RF-Based traffic detection and identification. In IEEE VTC Fall, 2012.Google ScholarCross Ref
- H. Aly et al. New insights into WiFi-based device-free localization. In ACM UbiComp, 2013. Google ScholarDigital Library
- T. Ballal et al. Breathing rate estimation from a non-contact biosensor using an adaptive IIR notch filter. In IEEE BioWireleSS, 2012.Google ScholarCross Ref
- Y. Chen and P. Rapajic. Human respiration rate estimation using ultra-wideband distributed cognitive radar system. International Journal of Automation and Computing, 2008.Google Scholar
- D. De Backer et al. Influence of respiratory rate on stroke volume variation in mechanically ventilated patients. Anesthesiology, 2009.Google Scholar
- Y. Ding et al. RFTraffic: passive traffic awareness based on emitted RF noise from the vehicles. In IEEE ITST, 2011.Google ScholarCross Ref
- A. D. Droitcour et al. Signal-to-noise ratio in doppler radar system for heart and respiratory rate measurements. IEEE Microwave Theory and Techniques, 2009.Google ScholarCross Ref
- K. El-Kafrawy et al. Propagation modeling for accurate indoor WLAN RSS-based localization. In IEEE VTC, 2010-Fall.Google Scholar
- K. El-Kafrawy et al. Impact of the human motion on the variance of the received signal strength of wireless links. In PIMRC, 2011.Google Scholar
- A. Eleryan et al. Synthetic generation of radio maps for device-free passive localization. In IEEE GLOBECOM, 2011.Google ScholarCross Ref
- A. S. Fauci et al. Harrison's principles of internal medicine. McGraw-Hill Medical New York, 2008.Google Scholar
- E. Jonathan and M. Leahy. Investigating a smartphone imaging unit for photoplethysmography. Physiological measurement, 2010.Google Scholar
- O. J. Kaltiokallio et al. Non-invasive respiration rate monitoring using a single COTS TX-RX pair. In IEEE IPSN, 2014. Google ScholarDigital Library
- N. Kassem et al. RF-based vehicle detection and speed estimation. In IEEE VTC Spring, 2012.Google ScholarCross Ref
- W. A. Knaus et al. APACHE II: a severity of disease classification system. Critical care medicine, 1985.Google ScholarCross Ref
- A. Kosba et al. Robust WLAN device-free passive motion detection. In IEEE WCNC, 2012.Google ScholarCross Ref
- A. E. Kosba et al. Analysis of a device-free passive tracking system in typical wireless environments. In IEEE NTMS, 2009. Google ScholarDigital Library
- A. E. Kosba et al. RASID: A robust WLAN device-free passive motion detection system. In IEEE PerCom, 2012.Google Scholar
- A. Lazaro et al. Analysis of vital signs monitoring using an IR-UWB radar. Progress In Electromagnetics Research, 2010.Google Scholar
- W. Lindh et al. Delmar's comprehensive medical assisting: administrative and clinical competencies. Cengage Learning, 2013.Google Scholar
- J. J. McKenna et al. Why babies should never sleep alone: a review of the co-sleeping controversy in relation to SIDS, bedsharing and breast feeding. Paediatric respiratory reviews, 2005.Google Scholar
- M. L. R. Mogue and B. Rantala. Capnometers. Journal of clinical monitoring, 1988.Google Scholar
- A. Mtibaa et al. Towards computational offloading in mobile device clouds. In IEEE CloudCom, 2013. Google ScholarDigital Library
- A. Mtibaa et al. Towards resource sharing in mobile device clouds: Power balancing across mobile devices. In ACM SIGCOMM MCC Workshop. ACM, 2013. Google ScholarDigital Library
- S. Nibhanupudi. Signal denoising using wavelets. PhD thesis, University of Cincinnati, 2003.Google Scholar
- R. Parkes. Rate of respiration: the forgotten vital sign: Racheal parkes explains why emergency department nurses should document the respiratory rates of all patients, irrespective of their presenting complaints. Emergency Nurse, 2011.Google Scholar
- N. Patwari et al. Breathfinding: A Wireless Network That Monitors and Locates Breathing in a Home. IEEE J-STSP, 2014.Google Scholar
- P. Pelegris et al. A novel method to detect heart beat rate using a mobile phone. In IEEE EMBC, 2010.Google ScholarCross Ref
- M.-Z. Poh et al. Non-contact, Automated Cardiac Pulse Measurements Using Video Imaging and Blind Source Separation. Optics Express, 2010.Google Scholar
- Q. Pu et al. Whole-home gesture recognition using wireless signals. In ACM MobiCom, 2013. Google ScholarDigital Library
- I. Sabek et al. ACE: An accurate and efficient multi-entity device-free WLAN localization system. IEEE Transactions on Mobile Computing, 2014.Google Scholar
- I. Sabek and M. Youssef. Multi-entity device-free WLAN localization. In IEEE GLOBECOM, 2012.Google ScholarCross Ref
- I. Sabek and M. Youssef. Spot: An accurate and efficient multi-entity device-free WLAN localization system. CoRR, 2012.Google Scholar
- A. Saeed et al. Ichnaea: A low-overhead robust WLAN device-free passive localization system. IEEE J-STSP, 2014.Google Scholar
- S. Sardy et al. Robust wavelet denoising. IEEE Signal Processing, 2001. Google ScholarDigital Library
- C. Scully and other. Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Biomedical Engineering, 2012.Google ScholarCross Ref
- M. Seifeldin et al. Nuzzer: A large-scale device-free passive localization system for wireless environments. IEEE Transactions on Mobile Computing, 2013. Google ScholarDigital Library
- M. Seifeldin and M. Youssef. A deterministic large-scale device-free passive localization system for wireless environments. In ACM PETRA, 2010. Google ScholarDigital Library
- N. H. Shariati and E. Zahedi. Comparison of selected parametric models for analysis of the photoplethysmographic signal. In IEEE CCSP, 2005.Google ScholarCross Ref
- S. Sigg et al. RF-based device-free recognition of simultaneously conducted activities. In ACM UbiComp, 2013. Google ScholarDigital Library
- J. Yang, Y. Ge, et al. Performing joint learning for passive intrusion detection in pervasive wireless environments. In IEEE INFOCOM, 2010. Google ScholarDigital Library
- M. Youssef et al. Challenges: device-free passive localization for wireless environments. In ACM MobiCom. ACM, 2007. Google ScholarDigital Library
- G. Yuan et al. Respiratory rate and breathing pattern. The McMaster University Medical Journal, 2013.Google Scholar
Index Terms
- UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator
Recommendations
RF powered sleep apnea monitoring system
PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive EnvironmentsSleep Apnea is a chronic and widespread problem that is characterized by periods of pauses in breathing during sleep. It can lead to several life threatening conditions if left undiagnosed. Conventional methods of diagnosing sleep apnea involve ...
A cost-effective and non-invasive system for sleep and vital signs monitoring using passive RFID tags
MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and ServicesInadequate sleep has very bad impacts on human daily life activities. Sleep disorders are one of the main reasons for the inadequate sleep problem. Diagnosis of these disorders is a very challenging task because they occur at night when the patients are ...
ApneaDetector: Detecting Sleep Apnea with Smartwatches
Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, ...
Comments