Evaluation of a real-time low-power cardiorespiratory sensor for the IoT

Arthur Gatouillat, Bertrand Massot, Youakim Badr, Ervin Sejdic, Claudine Gehin

Research output: Contribution to journalArticle

Abstract

A wide variety of sensors have been developed in the biomedical engineering community for telemedicine and personalized healthcare applications. However, they usually focus on sensor connectivity and embedded signal processing, at the expense of the sensing part. This observation lead to the development and exhaustive evaluation of a new ECGbased cardiorespiratory IoT sensor. In order to improve the robustness of our IoT-based sensor, we discuss in detail the influence of electrodes placement and nature. Performance assessment of our sensor resulted in a best-case sensitivity of 99.95% and a precision of 99.89% for an abdominal positioning of wet electrodes, while a sensitivity of 99.47% and a precision of 99.31% were observed using a commercialgrade dry electrodes belt. Consequently, we prove that our sensor is fit for the comfortable medical-grade monitoring of the cardiorespiratory activity in order to provide insights of patients health in a telemedicine context.

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Electrodes
Telemedicine
Sensors
Biomedical Engineering
Delivery of Health Care
Biomedical engineering
Health
Internet of things
Signal processing
Monitoring

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

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title = "Evaluation of a real-time low-power cardiorespiratory sensor for the IoT",
abstract = "A wide variety of sensors have been developed in the biomedical engineering community for telemedicine and personalized healthcare applications. However, they usually focus on sensor connectivity and embedded signal processing, at the expense of the sensing part. This observation lead to the development and exhaustive evaluation of a new ECGbased cardiorespiratory IoT sensor. In order to improve the robustness of our IoT-based sensor, we discuss in detail the influence of electrodes placement and nature. Performance assessment of our sensor resulted in a best-case sensitivity of 99.95{\%} and a precision of 99.89{\%} for an abdominal positioning of wet electrodes, while a sensitivity of 99.47{\%} and a precision of 99.31{\%} were observed using a commercialgrade dry electrodes belt. Consequently, we prove that our sensor is fit for the comfortable medical-grade monitoring of the cardiorespiratory activity in order to provide insights of patients health in a telemedicine context.",
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AU - Gatouillat, Arthur

AU - Massot, Bertrand

AU - Badr, Youakim

AU - Sejdic, Ervin

AU - Gehin, Claudine

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