ComplexBeat: Breathing Rate Estimation from Complex CSI

Sitian Li; Andreas Toftegaard Kristensen; Andreas Burg; Alexios Balatsoukas-Stimming

doi:10.1109/SiPS52927.2021.00046

ComplexBeat: Breathing Rate Estimation from Complex CSI

Sitian Li
, Andreas Toftegaard Kristensen
, Andreas Burg
, Alexios Balatsoukas-Stimming

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

11 Citations (Scopus)

1 Downloads (Pure)

Abstract

In this paper, we explore the use of channel state information (CSI) from a WiFi system to estimate the breathing rate of a person in a room. In order to extract WiFi CSI components that are sensitive to breathing, we propose to consider the delay domain channel impulse response (CIR), while most state-of-the-art methods consider its frequency domain representation. One obstacle while processing the CSI data is that its amplitude and phase are highly distorted by measurement uncertainties. We thus also propose an amplitude calibration method and a phase offset calibration method for CSI measured in orthogonal frequency-division multiplexing (OFDM) multiple- input multiple-output (MIMO) systems. Finally, we implement a complete breathing rate estimation system in order to showcase the effectiveness of our proposed calibration and CSI extraction methods.

Original language	English
Title of host publication	2021 IEEE Workshop on Signal Processing Systems (SiPS)
Publisher	Institute of Electrical and Electronics Engineers
Pages	217-222
Number of pages	6
ISBN (Electronic)	978-1-6654-0144-9
DOIs	https://doi.org/10.1109/SiPS52927.2021.00046
Publication status	Published - 13 Nov 2021
Event	35th IEEE Workshop on Signal Processing Systems, SiPS 2021 - Coimbra, Portugal Duration: 19 Oct 2021 → 21 Oct 2021 Conference number: 35

Conference

Conference	35th IEEE Workshop on Signal Processing Systems, SiPS 2021
Abbreviated title	SiPS 2021
Country/Territory	Portugal
City	Coimbra
Period	19/10/21 → 21/10/21

Bibliographical note

Funding Information:
VI. CONCLUSIONS In this paper, we show that robust breathing rate estimation from CSI relies on careful selection of the best CSI component, especially in the delay domain. To this end, we introduce a dominant path-based CSI amplitude calibration method and a linear component-based CSI phase calibration method for preprocessing of the CSI data. The calibration methods combined with the delay domain filtering leads to a better CSI extraction compared to PhaseBeat and its variants.Furthermore, we show that the breathing rate can be estimated directly from calibrated complex-valued CSI or CIR. Compared to observing only the phase values, the estimation from complex-valued CSI achieves the highest accuracy. ACKNOWLEDGMENT This research has been kindly supported by the Swiss National Science Foundation under Grant-ID 182621. REFERENCES [1] S. Rolfe, “The importance of respiratory rate monitor-ing,” British Journal of Nursing, vol. 28, no. 8, pp. 504– 508, Apr. 2019. [2] S. R. Pandi-Perumal, D. W. Spence, and A. S. Ba-Hammam, “Polysomnography: An overview,” in Primary Care Sleep Medicine, J. F. Pagel and S. R. Pandi-Perumal, Eds. Springer New York, 2014, pp. 29–42. [3] S.Costanzo,“Software-definedDopplerradarsensorfor human breathing detection”, Sensors, vol. 19, no. 14, p. 3085, Jul. 2019. [4] A. Droitcour, O. Boric-Lubecke, and G. Kovacs, “Signal-to-Noise Ratio in Doppler radar system for heart and respiratory rate measurements”, IEEE Transactions on Microwave Theory and Techniques, vol. 57, no. 10, pp. 2498–2507, Oct. 2009. [5] L. Anitori, A. de Jong, and F. Nennie, “FMCW radar for life-sign detection”, in 2009 IEEE Radar Conference. IEEE, 2009, pp. 1–6. [6] Z. Peng, J. M. Munoz-Ferreras, Y. Tang, C. Liu, R. Gomez-Garcia, L. Ran, and C. Li, “A portable FMCW interferometry radar with programmable Low-IF archi-tecture for localization, ISAR imaging, and vital sign tracking,” IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 4, pp. 1334–1344, Apr. 2017. [7] J. Liu, Y. Wang, Y. Chen, J. Yang, X. Chen, and J. Cheng, “Tracking vital signs during sleep leveraging off-the-shelf wifi,” in Proceedingsof the 16th ACM International Symposium on Mobile Ad Hoc Networking and Com-puting, ser. MobiHoc ’15. Association for Computing Machinery, 2015, p. 267276. [8] Y. Ma, G. Zhou, and S. Wang, “WiFi sensing with channel state information: A survey,” ACM Computing Surveys, vol. 52, no. 3, pp. 1–36, Jul. 2019. [9] S. Lee, Y.-D. Park, Y.-J. Suh, and S. Jeon, “Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals,” in 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE, Jan. 2018, pp. 1–7. [10] P.Hillyard,A.Luong,A.S.Abrar,N.Patwari,K.Sundar, R. Farney, J. Burch, C. A. Porucznik, and S. H. Pollard,

Publisher Copyright:
© 2021 IEEE.

Funding

VI. CONCLUSIONS In this paper, we show that robust breathing rate estimation from CSI relies on careful selection of the best CSI component, especially in the delay domain. To this end, we introduce a dominant path-based CSI amplitude calibration method and a linear component-based CSI phase calibration method for preprocessing of the CSI data. The calibration methods combined with the delay domain filtering leads to a better CSI extraction compared to PhaseBeat and its variants.Furthermore, we show that the breathing rate can be estimated directly from calibrated complex-valued CSI or CIR. Compared to observing only the phase values, the estimation from complex-valued CSI achieves the highest accuracy. ACKNOWLEDGMENT This research has been kindly supported by the Swiss National Science Foundation under Grant-ID 182621. REFERENCES [1] S. Rolfe, “The importance of respiratory rate monitor-ing,” British Journal of Nursing, vol. 28, no. 8, pp. 504– 508, Apr. 2019. [2] S. R. Pandi-Perumal, D. W. Spence, and A. S. Ba-Hammam, “Polysomnography: An overview,” in Primary Care Sleep Medicine, J. F. Pagel and S. R. Pandi-Perumal, Eds. Springer New York, 2014, pp. 29–42. [3] S.Costanzo,“Software-definedDopplerradarsensorfor human breathing detection”, Sensors, vol. 19, no. 14, p. 3085, Jul. 2019. [4] A. Droitcour, O. Boric-Lubecke, and G. Kovacs, “Signal-to-Noise Ratio in Doppler radar system for heart and respiratory rate measurements”, IEEE Transactions on Microwave Theory and Techniques, vol. 57, no. 10, pp. 2498–2507, Oct. 2009. [5] L. Anitori, A. de Jong, and F. Nennie, “FMCW radar for life-sign detection”, in 2009 IEEE Radar Conference. IEEE, 2009, pp. 1–6. [6] Z. Peng, J. M. Munoz-Ferreras, Y. Tang, C. Liu, R. Gomez-Garcia, L. Ran, and C. Li, “A portable FMCW interferometry radar with programmable Low-IF archi-tecture for localization, ISAR imaging, and vital sign tracking,” IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 4, pp. 1334–1344, Apr. 2017. [7] J. Liu, Y. Wang, Y. Chen, J. Yang, X. Chen, and J. Cheng, “Tracking vital signs during sleep leveraging off-the-shelf wifi,” in Proceedingsof the 16th ACM International Symposium on Mobile Ad Hoc Networking and Com-puting, ser. MobiHoc ’15. Association for Computing Machinery, 2015, p. 267276. [8] Y. Ma, G. Zhou, and S. Wang, “WiFi sensing with channel state information: A survey,” ACM Computing Surveys, vol. 52, no. 3, pp. 1–36, Jul. 2019. [9] S. Lee, Y.-D. Park, Y.-J. Suh, and S. Jeon, “Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals,” in 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE, Jan. 2018, pp. 1–7. [10] P.Hillyard,A.Luong,A.S.Abrar,N.Patwari,K.Sundar, R. Farney, J. Burch, C. A. Porucznik, and S. H. Pollard,

Keywords

Channel state information
OFDM amplitude calibration
OFDM phase calibration
Vital signs monitoring
WiFi OFDM sensing

Access to Document

10.1109/SiPS52927.2021.00046

Cite this

@inproceedings{98e095b1fc1f4f53bd96f38c87bc3f20,

title = "ComplexBeat: Breathing Rate Estimation from Complex CSI",

abstract = "In this paper, we explore the use of channel state information (CSI) from a WiFi system to estimate the breathing rate of a person in a room. In order to extract WiFi CSI components that are sensitive to breathing, we propose to consider the delay domain channel impulse response (CIR), while most state-of-the-art methods consider its frequency domain representation. One obstacle while processing the CSI data is that its amplitude and phase are highly distorted by measurement uncertainties. We thus also propose an amplitude calibration method and a phase offset calibration method for CSI measured in orthogonal frequency-division multiplexing (OFDM) multiple- input multiple-output (MIMO) systems. Finally, we implement a complete breathing rate estimation system in order to showcase the effectiveness of our proposed calibration and CSI extraction methods. ",

keywords = "Channel state information, OFDM amplitude calibration, OFDM phase calibration, Vital signs monitoring, WiFi OFDM sensing",

author = "Sitian Li and Kristensen, \{Andreas Toftegaard\} and Andreas Burg and Alexios Balatsoukas-Stimming",

note = "Funding Information: VI. CONCLUSIONS In this paper, we show that robust breathing rate estimation from CSI relies on careful selection of the best CSI component, especially in the delay domain. To this end, we introduce a dominant path-based CSI amplitude calibration method and a linear component-based CSI phase calibration method for preprocessing of the CSI data. The calibration methods combined with the delay domain filtering leads to a better CSI extraction compared to PhaseBeat and its variants.Furthermore, we show that the breathing rate can be estimated directly from calibrated complex-valued CSI or CIR. Compared to observing only the phase values, the estimation from complex-valued CSI achieves the highest accuracy. ACKNOWLEDGMENT This research has been kindly supported by the Swiss National Science Foundation under Grant-ID 182621. REFERENCES [1] S. Rolfe, {\textquotedblleft}The importance of respiratory rate monitor-ing,{\textquotedblright} British Journal of Nursing, vol. 28, no. 8, pp. 504{\textendash} 508, Apr. 2019. [2] S. R. Pandi-Perumal, D. W. Spence, and A. S. Ba-Hammam, {\textquotedblleft}Polysomnography: An overview,{\textquotedblright} in Primary Care Sleep Medicine, J. F. Pagel and S. R. Pandi-Perumal, Eds. Springer New York, 2014, pp. 29{\textendash}42. [3] S.Costanzo,{\textquotedblleft}Software-definedDopplerradarsensorfor human breathing detection{\textquotedblright}, Sensors, vol. 19, no. 14, p. 3085, Jul. 2019. [4] A. Droitcour, O. Boric-Lubecke, and G. Kovacs, {\textquotedblleft}Signal-to-Noise Ratio in Doppler radar system for heart and respiratory rate measurements{\textquotedblright}, IEEE Transactions on Microwave Theory and Techniques, vol. 57, no. 10, pp. 2498{\textendash}2507, Oct. 2009. [5] L. Anitori, A. de Jong, and F. Nennie, {\textquotedblleft}FMCW radar for life-sign detection{\textquotedblright}, in 2009 IEEE Radar Conference. IEEE, 2009, pp. 1{\textendash}6. [6] Z. Peng, J. M. Munoz-Ferreras, Y. Tang, C. Liu, R. Gomez-Garcia, L. Ran, and C. Li, {\textquotedblleft}A portable FMCW interferometry radar with programmable Low-IF archi-tecture for localization, ISAR imaging, and vital sign tracking,{\textquotedblright} IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 4, pp. 1334{\textendash}1344, Apr. 2017. [7] J. Liu, Y. Wang, Y. Chen, J. Yang, X. Chen, and J. Cheng, {\textquotedblleft}Tracking vital signs during sleep leveraging off-the-shelf wifi,{\textquotedblright} in Proceedingsof the 16th ACM International Symposium on Mobile Ad Hoc Networking and Com-puting, ser. MobiHoc {\textquoteright}15. Association for Computing Machinery, 2015, p. 267276. [8] Y. Ma, G. Zhou, and S. Wang, {\textquotedblleft}WiFi sensing with channel state information: A survey,{\textquotedblright} ACM Computing Surveys, vol. 52, no. 3, pp. 1{\textendash}36, Jul. 2019. [9] S. Lee, Y.-D. Park, Y.-J. Suh, and S. Jeon, {\textquotedblleft}Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals,{\textquotedblright} in 2018 15th IEEE Annual Consumer Communications \& Networking Conference (CCNC). IEEE, Jan. 2018, pp. 1{\textendash}7. [10] P.Hillyard,A.Luong,A.S.Abrar,N.Patwari,K.Sundar, R. Farney, J. Burch, C. A. Porucznik, and S. H. Pollard, Publisher Copyright: {\textcopyright} 2021 IEEE.; 35th IEEE Workshop on Signal Processing Systems, SiPS 2021, SiPS 2021 ; Conference date: 19-10-2021 Through 21-10-2021",

year = "2021",

month = nov,

day = "13",

doi = "10.1109/SiPS52927.2021.00046",

language = "English",

pages = "217--222",

booktitle = "2021 IEEE Workshop on Signal Processing Systems (SiPS)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

}

Li, S, Kristensen, AT, Burg, A & Balatsoukas-Stimming, A 2021, ComplexBeat: Breathing Rate Estimation from Complex CSI. in 2021 IEEE Workshop on Signal Processing Systems (SiPS)., 9604942, Institute of Electrical and Electronics Engineers, pp. 217-222, 35th IEEE Workshop on Signal Processing Systems, SiPS 2021, Coimbra, Portugal, 19/10/21. https://doi.org/10.1109/SiPS52927.2021.00046

ComplexBeat: Breathing Rate Estimation from Complex CSI. / Li, Sitian; Kristensen, Andreas Toftegaard; Burg, Andreas et al.
2021 IEEE Workshop on Signal Processing Systems (SiPS). Institute of Electrical and Electronics Engineers, 2021. p. 217-222 9604942.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - ComplexBeat

T2 - 35th IEEE Workshop on Signal Processing Systems, SiPS 2021

AU - Li, Sitian

AU - Kristensen, Andreas Toftegaard

AU - Burg, Andreas

AU - Balatsoukas-Stimming, Alexios

N1 - Conference code: 35

PY - 2021/11/13

Y1 - 2021/11/13

N2 - In this paper, we explore the use of channel state information (CSI) from a WiFi system to estimate the breathing rate of a person in a room. In order to extract WiFi CSI components that are sensitive to breathing, we propose to consider the delay domain channel impulse response (CIR), while most state-of-the-art methods consider its frequency domain representation. One obstacle while processing the CSI data is that its amplitude and phase are highly distorted by measurement uncertainties. We thus also propose an amplitude calibration method and a phase offset calibration method for CSI measured in orthogonal frequency-division multiplexing (OFDM) multiple- input multiple-output (MIMO) systems. Finally, we implement a complete breathing rate estimation system in order to showcase the effectiveness of our proposed calibration and CSI extraction methods.

AB - In this paper, we explore the use of channel state information (CSI) from a WiFi system to estimate the breathing rate of a person in a room. In order to extract WiFi CSI components that are sensitive to breathing, we propose to consider the delay domain channel impulse response (CIR), while most state-of-the-art methods consider its frequency domain representation. One obstacle while processing the CSI data is that its amplitude and phase are highly distorted by measurement uncertainties. We thus also propose an amplitude calibration method and a phase offset calibration method for CSI measured in orthogonal frequency-division multiplexing (OFDM) multiple- input multiple-output (MIMO) systems. Finally, we implement a complete breathing rate estimation system in order to showcase the effectiveness of our proposed calibration and CSI extraction methods.

KW - Channel state information

KW - OFDM amplitude calibration

KW - OFDM phase calibration

KW - Vital signs monitoring

KW - WiFi OFDM sensing

UR - https://www.scopus.com/pages/publications/85122875023

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DO - 10.1109/SiPS52927.2021.00046

M3 - Conference contribution

AN - SCOPUS:85122875023

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EP - 222

BT - 2021 IEEE Workshop on Signal Processing Systems (SiPS)

PB - Institute of Electrical and Electronics Engineers

Y2 - 19 October 2021 through 21 October 2021

ER -

ComplexBeat: Breathing Rate Estimation from Complex CSI

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Keywords

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