ABSTRACT
Tracking human vital signs of breathing and heart rates during sleep is important as it can help to assess the general physical health of a person and provide useful clues for diagnosing possible diseases. Traditional approaches (e.g., Polysomnography (PSG)) are limited to clinic usage. Recent radio frequency (RF) based approaches require specialized devices or dedicated wireless sensors and are only able to track breathing rate. In this work, we propose to track the vital signs of both breathing rate and heart rate during sleep by using off-the-shelf WiFi without any wearable or dedicated devices. Our system re-uses existing WiFi network and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. The developed algorithm makes use of the channel information in both time and frequency domain to estimate breathing and heart rates, and it works well when either individual or two persons are in bed. Our extensive experiments demonstrate that our system can accurately capture vital signs during sleep under realistic settings, and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing non-invasive, continuous fine-grained vital signs monitoring without any additional cost.
- "Sleep apnea: What is sleep apnea?" NHLBI: Health Information for the Public. U.S. Department of Health and Human Services, 2010.Google Scholar
- P. X. Braun, C. F. Gmachl, and R. A. Dweik, "Bridging the collaborative gap: Realizing the clinical potential of breath analysis for disease diagnosis and monitoring--tutorial," IEEE Sensors Journal, vol. 12, no. 11, pp. 3258--3270, 2012.Google ScholarCross Ref
- G. S. Chung, B. H. Choi, K. K. Kim, Y. G. Lim, J. W. Choi, D.-U. Jeong, and K. S. Park, "Rem sleep classification with respiration rates," in 6th International Special Topic Conference on Information Technology Applications in Biomedicine (ITAB). IEEE, 2007, pp. 194--197.Google Scholar
- C. A. Kushida, M. R. Littner, T. Morgenthaler, C. A. Alessi, D. Bailey, J. Coleman Jr, L. Friedman, M. Hirshkowitz, S. Kapen, M. Kramer phet al., "Practice parameters for the indications for polysomnography and related procedures: an update for 2005," Sleep, vol. 28, no. 4, pp. 499--521, 2005.Google ScholarCross Ref
- Y. Chen, D. Misra, H. Wang, H.-R. Chuang, and E. Postow, "An x-band microwave life-detection system," IEEE Transactions on Biomedical Engineering, vol. 33, no. 7, pp. 697--701, 1986.Google ScholarCross Ref
- J. Salmi and A. F. Molisch, "Propagation parameter estimation, modeling and measurements for ultrawideband mimo radar," IEEE Transactions on Antennas and Propagation, vol. 59, no. 11, pp. 4257--4267, 2011.Google ScholarCross Ref
- F. Adib, Z. Kabelac, H. Mao, D. Katabi, and R. C. Miller, "Demo: Real-time breath monitoring using wireless signals," in MobiCom, 2014. Google ScholarDigital Library
- F. Adib, Z. Kabelac, and D. Katabi, "Multi-person motion tracking via rf body reflections," MIT technical report, 2014.Google Scholar
- N. Patwari, L. Brewer, Q. Tate, O. Kaltiokallio, and M. Bocca, "Breathfinding: A wireless network that monitors andlocates breathing in a home," IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 1, pp. 30--42, 2014.Google ScholarCross Ref
- O. J. Kaltiokallio, H. Yigitler, R. Jäntti, and N. Patwari, "Non-invasive respiration rate monitoring using a single cots tx-rx pai" in IPSN, 2014, pp. 59--70. Google ScholarDigital Library
- Fitbit, http://www.fitbit.com/.Google Scholar
- Jawbone Up, https://jawbone.com/up.Google Scholar
- Sleep as Android, https://sites.google.com/site/sleepasandroid/.Google Scholar
- T. Hao, G. Xing, and G. Zhou, "isleep: unobtrusive sleep quality monitoring using smartphones," in Sensys, 2013. Google ScholarDigital Library
- SleepIQ, http://bamlabs.com/.Google Scholar
- J. Penne, C. Schaller, J. Hornegger, and T. Kuwert, "Robust real-time 3d respiratory motion detection using time-of-flight cameras," International Journal of Computer Assisted Radiology and Surgery, vol. 3, no. 5, pp. 427--431, 2008.Google ScholarCross Ref
- Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. Liu, "E-eyes: device-free location-oriented activity identification using fine-grained wifi signatures," in MobiCom, 2014, pp. 617--628. Google ScholarDigital Library
- J. F. Murray, The normal lung: the basis for diagnosis and treatment of pulmonary disease. Saunders, 1986.Google Scholar
- P. Sebel, M. Stoddart, R. Waldhorn, C. Waldman, and P. Whitfield, Respiration, the breath of life. Torstar Books New York, 1985.Google Scholar
- "Target heart rates - aha," Target Heart Rates. American Heart Association, 2014.Google Scholar
- L. Davies and U. Gather, "The identification of multiple outliers," Journal of the American Statistical Association, vol. 88, no. 423, pp. 782--792, 1993.Google ScholarCross Ref
- "NEULOG Respiration Monitor Logger Sensor," http://www.neulog.com/.Google Scholar
- W. Xi, J. Zhao, X. Li, K. Zhao, S. Tang, X. Liu, and Z. J., "Electronic frog eye: Counting crowd using wifi," in INFOCOM, 2014.Google Scholar
- D. Halperin, W. Hu, A. Sheth, and D. Wetherall, "Tool release: gathering 802.11 n traces with channel state information," ACM SIGCOMM Computer Communication Review, vol. 41, no. 1, pp. 53--53, 2011. Google ScholarDigital Library
- "Sleep position gives personality clue," 2003, http://news.bbc.co.uk/2/hi/health/3112170.stm.Google Scholar
- Zephyr Technology, http://zephyranywhere.com/.Google Scholar
Index Terms
- Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi
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