Synthesizing Wider WiFi Bandwidth for Respiration Rate Monitoring in Dynamic Environments

Respiration rate monitoring is beneficial for the diagnosis of a variety of diseases, such as heart failure and sleep disorders. Radio Frequency (RF) based respiration rate monitoring systems, namely ultra-wideband radar and COTS device, have been proposed without requiring any direct contact with the detected person. However, existing RF based systems either require expensive UWB radio (radar based) or work only in stationary environments (COTS device based). To address the limitations of both radar based and COTS device based systems, in this paper, we propose RespiRadio, a system that can detect a person’s respiration rate in dynamic ambient environments via a single TX-RX pair of WiFi cards. The key novelty of RespiRadio is that it overcomes the limit of existing COTS device based respiration rate systems by synthesizing a wider-bandwidth WiFi radio. With the synthesized WiFi radio, we can identify the path reflected by the breathing person and then analyze the periodicity of the signal power measurements only from this path to infer the respiration rate. We experimentally evaluate the performance of RespiRadio in non-static indoor environments and the results demonstrate that the overall estimation error is 0.152 breaths per minute (bpm).

[1]  R. Plackett,et al.  Introduction to Statistical Analysis. , 1952 .

[2]  Peter D. Wagner,et al.  THE NORMAL LUNG: THE BASIS FOR DIAGNOSIS AND TREATMENT OF PULMONARY DISEASE , 1987 .

[3]  C. Guilleminault,et al.  Recognition of sleep-disordered breathing in children. , 1996, Pediatrics.

[4]  S. Javaheri,et al.  Sleep apnea in 81 ambulatory male patients with stable heart failure. Types and their prevalences, consequences, and presentations. , 1998, Circulation.

[5]  Hristo D. Hristov,et al.  Fresnal Zones in Wireless Links, Zone Plate Lenses and Antennas , 2000 .

[6]  James E. Whitney,et al.  Respiration rate signal extraction from heart rate , 2001, SPIE Defense + Commercial Sensing.

[7]  Matthew S. Gast,et al.  802.11 Wireless Networks: The Definitive Guide , 2002 .

[8]  Frank H Wilhelm,et al.  The LifeShirt , 2003, Behavior modification.

[9]  S. Venkatesh,et al.  Implementation and analysis of respiration-rate estimation using impulse-based UWB , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[10]  R. Michael Buehrer,et al.  Multi-target estimation of heart and respiration rates using ultra wideband sensors , 2006, 2006 14th European Signal Processing Conference.

[11]  S. Särkkä,et al.  On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems , 2007, IEEE Transactions on Automatic Control.

[12]  C.R. Merritt,et al.  Textile-Based Capacitive Sensors for Respiration Monitoring , 2009, IEEE Sensors Journal.

[13]  Niki Fens,et al.  Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. , 2009, American journal of respiratory and critical care medicine.

[14]  Neal Patwari,et al.  Radio Tomographic Imaging with Wireless Networks , 2010, IEEE Transactions on Mobile Computing.

[15]  David Wetherall,et al.  Predictable 802.11 packet delivery from wireless channel measurements , 2010, SIGCOMM '10.

[16]  Jian Li,et al.  Accurate Doppler Radar Noncontact Vital Sign Detection Using the RELAX Algorithm , 2010, IEEE Transactions on Instrumentation and Measurement.

[17]  A. F. Molisch,et al.  Propagation Parameter Estimation, Modeling and Measurements for Ultrawideband MIMO Radar , 2011, IEEE Transactions on Antennas and Propagation.

[18]  Frédo Durand,et al.  Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..

[19]  Simo Särkkä,et al.  Bayesian Filtering and Smoothing , 2013, Institute of Mathematical Statistics textbooks.

[20]  Sneha Kumar Kasera,et al.  Monitoring Breathing via Signal Strength in Wireless Networks , 2011, IEEE Transactions on Mobile Computing.

[21]  Ossi Kaltiokallio,et al.  Non-invasive respiration rate monitoring using a single COTS TX-RX pair , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[22]  J. Wheatley,et al.  Validation of the Sonomat: a contactless monitoring system used for the diagnosis of sleep disordered breathing. , 2014, Sleep.

[23]  Yusheng Ji,et al.  RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals , 2014, IEEE Transactions on Mobile Computing.

[24]  Kyu-Han Kim,et al.  SAIL: single access point-based indoor localization , 2014, MobiSys.

[25]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[26]  Rob Miller,et al.  Smart Homes that Monitor Breathing and Heart Rate , 2015, CHI.

[27]  Xu Chen,et al.  Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi , 2015, MobiHoc.

[28]  Swarun Kumar,et al.  Decimeter-Level Localization with a Single WiFi Access Point , 2016, NSDI.

[29]  Dan Wu,et al.  Human respiration detection with commodity wifi devices: do user location and body orientation matter? , 2016, UbiComp.

[30]  Xingshe Zhou,et al.  C-FMCW Based Contactless Respiration Detection Using Acoustic Signal , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[31]  Zhenjiang Li,et al.  aLeak: Privacy Leakage through Context - Free Wearable Side-Channel , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[32]  Yusheng Ji,et al.  Accurate Location Tracking From CSI-Based Passive Device-Free Probabilistic Fingerprinting , 2018, IEEE Transactions on Vehicular Technology.

[33]  Mo Li,et al.  Precise Power Delay Profiling with Commodity Wi-Fi , 2015, IEEE Transactions on Mobile Computing.

[34]  Yuanqing Zheng,et al.  TagBreathe: Monitor Breathing with Commodity RFID Systems , 2020, IEEE Transactions on Mobile Computing.