HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System

Zhenhua Jia, Musaab Alaziz, Xiang Chi, Richard E. Howard, Yanyong Zhang, Pei Zhang, Wade Trappe, Anand Sivasubramaniam, Ning An

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

Heartbeat monitoring during sleep is critically important to ensuring the well-being of many people, ranging from patients to elderly. Technologies that support heartbeat monitoring should be unobtrusive, and thus solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. We tackle the challenge of accurate, low-cost and easy to deploy heartbeat monitoring by investigating whether off-the-shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. At the same time, lower-frequency movements such as respiration, can be naturally filtered out by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We have built a prototype sensor and conducted detailed experiments that involve 43 subjects (with IRB approval), which demonstrate that the geophone sensor is a compelling solution to long-term at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30% over 500 data samples when the subjects were still on the bed, and 3.87% over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25% over more than 181 hours' data.

Original languageEnglish (US)
Title of host publication2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509008025
DOIs
StatePublished - Apr 26 2016
Event15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Vienna, Austria
Duration: Apr 11 2016Apr 14 2016

Publication series

Name2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings

Other

Other15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016
CountryAustria
CityVienna
Period4/11/164/14/16

Fingerprint

Seismographs
Monitoring
Sensors
Oximeters
Error analysis
Signal processing
Costs
Experiments

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

Cite this

Jia, Z., Alaziz, M., Chi, X., Howard, R. E., Zhang, Y., Zhang, P., ... An, N. (2016). HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System. In 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings [7460676] (2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPSN.2016.7460676
Jia, Zhenhua ; Alaziz, Musaab ; Chi, Xiang ; Howard, Richard E. ; Zhang, Yanyong ; Zhang, Pei ; Trappe, Wade ; Sivasubramaniam, Anand ; An, Ning. / HB-Phone : A Bed-Mounted Geophone-Based Heartbeat Monitoring System. 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. (2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings).
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title = "HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System",
abstract = "Heartbeat monitoring during sleep is critically important to ensuring the well-being of many people, ranging from patients to elderly. Technologies that support heartbeat monitoring should be unobtrusive, and thus solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. We tackle the challenge of accurate, low-cost and easy to deploy heartbeat monitoring by investigating whether off-the-shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. At the same time, lower-frequency movements such as respiration, can be naturally filtered out by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We have built a prototype sensor and conducted detailed experiments that involve 43 subjects (with IRB approval), which demonstrate that the geophone sensor is a compelling solution to long-term at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30{\%} over 500 data samples when the subjects were still on the bed, and 3.87{\%} over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25{\%} over more than 181 hours' data.",
author = "Zhenhua Jia and Musaab Alaziz and Xiang Chi and Howard, {Richard E.} and Yanyong Zhang and Pei Zhang and Wade Trappe and Anand Sivasubramaniam and Ning An",
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Jia, Z, Alaziz, M, Chi, X, Howard, RE, Zhang, Y, Zhang, P, Trappe, W, Sivasubramaniam, A & An, N 2016, HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System. in 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings., 7460676, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016, Vienna, Austria, 4/11/16. https://doi.org/10.1109/IPSN.2016.7460676

HB-Phone : A Bed-Mounted Geophone-Based Heartbeat Monitoring System. / Jia, Zhenhua; Alaziz, Musaab; Chi, Xiang; Howard, Richard E.; Zhang, Yanyong; Zhang, Pei; Trappe, Wade; Sivasubramaniam, Anand; An, Ning.

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7460676 (2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T2 - A Bed-Mounted Geophone-Based Heartbeat Monitoring System

AU - Jia, Zhenhua

AU - Alaziz, Musaab

AU - Chi, Xiang

AU - Howard, Richard E.

AU - Zhang, Yanyong

AU - Zhang, Pei

AU - Trappe, Wade

AU - Sivasubramaniam, Anand

AU - An, Ning

PY - 2016/4/26

Y1 - 2016/4/26

N2 - Heartbeat monitoring during sleep is critically important to ensuring the well-being of many people, ranging from patients to elderly. Technologies that support heartbeat monitoring should be unobtrusive, and thus solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. We tackle the challenge of accurate, low-cost and easy to deploy heartbeat monitoring by investigating whether off-the-shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. At the same time, lower-frequency movements such as respiration, can be naturally filtered out by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We have built a prototype sensor and conducted detailed experiments that involve 43 subjects (with IRB approval), which demonstrate that the geophone sensor is a compelling solution to long-term at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30% over 500 data samples when the subjects were still on the bed, and 3.87% over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25% over more than 181 hours' data.

AB - Heartbeat monitoring during sleep is critically important to ensuring the well-being of many people, ranging from patients to elderly. Technologies that support heartbeat monitoring should be unobtrusive, and thus solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. We tackle the challenge of accurate, low-cost and easy to deploy heartbeat monitoring by investigating whether off-the-shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. At the same time, lower-frequency movements such as respiration, can be naturally filtered out by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We have built a prototype sensor and conducted detailed experiments that involve 43 subjects (with IRB approval), which demonstrate that the geophone sensor is a compelling solution to long-term at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30% over 500 data samples when the subjects were still on the bed, and 3.87% over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25% over more than 181 hours' data.

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Jia Z, Alaziz M, Chi X, Howard RE, Zhang Y, Zhang P et al. HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System. In 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7460676. (2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 - Proceedings). https://doi.org/10.1109/IPSN.2016.7460676