RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

Hostettler, Roland; Kaltiokallio, Ossi; Yi̇ği̇tler, Hüseyi̇n; Särkkä, Simo; Jäntti, Riku

doi:10.23919/eusipco.2017.8081208

Cited by 10 publications

(13 citation statements)

References 16 publications

(24 reference statements)

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“…This result suggests that for low SNR operating region batch DFT estimator is better whereas for high SNR conditions GP is better in terms of accuracy. It is also to be noted that GP has other advantages, most notably it relaxes data acquisition requirements by not requiring uniform sampling, operating with occasional packet losses, and better fusing the measurements from different communication channels [14]. Therefore, it has an utmost importance to investigate achievable performance, and to select an appropriate estimator.…”

Section: B Evaluation Methodologymentioning

confidence: 99%

RSS Models for Respiration Rate Monitoring

Yi̇ği̇tler

Kaltiokallio

Hostettler

et al. 2020

IEEE Trans. on Mobile Comput.

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Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-tonoise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The implications of the model are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.

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Section: B Evaluation Methodologymentioning

confidence: 99%

RSS Models for Respiration Rate Monitoring

Yi̇ği̇tler

Kaltiokallio

Hostettler

et al. 2020

IEEE Trans. on Mobile Comput.

Self Cite

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“…It has also been shown that when one or many Zigbee links are nearby a breathing person, the RSS value of the links change as a person inhales and exhales [17,21,41,42,64]. IEEE 802.15.4 transceivers often make RSS values available to the application.…”

Section: Measurement Methodsmentioning

confidence: 99%

Experience

Hillyard

Luong

Abrar

et al. 2018

Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

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This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record groundtruth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.

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“…It has been shown that when one or many Zigbee links are nearby a breathing person, the RSS value of the links change as a person inhales and exhales [16,21,41,42,64]. IEEE 802.15.4 transceivers often make RSS values available to the application.…”

Section: Measurement Methodsmentioning

confidence: 99%

Comparing Respiratory Monitoring Performance of Commercial Wireless Devices

Hillyard,

Luong,

Abrar

et al. 2018

Preprint

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This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.

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RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

Cited by 10 publications

References 16 publications

RSS Models for Respiration Rate Monitoring

RSS Models for Respiration Rate Monitoring

Experience

Comparing Respiratory Monitoring Performance of Commercial Wireless Devices

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