A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-Based Wireless Wearable Sensors

William Mongan, Endla Anday, Genevieve Dion, Adam Fontecchio, Kelly Joyce, Timothy Kurzweg, Yuqiao Liu, Owen Montgomery, Ilhaan Rasheed, Cem Sahin, Shrenik Vora, Kapil Dandekar

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

10 Citations (Scopus)

Abstract

We have applied passive Radio Frequency Identification (RFID), typically used for inventory management, to implement a novel knit fabric strain gauge assembly using conductive thread. As the fabric antenna is stretched, the strength of the received signal varies, yielding potential for wearable, wireless, powerless smart-garment devices based on small and inexpensive passive RFID technology. Knit fabric sensors and other RFID biosensors can enable comfortable, continuous monitoring of biofeedback, but requires an integrated framework consisting of antenna modeling and fabrication, signal processing and machine learning on the noisy wireless signal, secure HIPAA- compliant data storage, visualization and human factors, and integration with existing medical devices and electronic health records (EHR) systems. We present a multidisciplinary, end-to-end framework to study, model, develop, and deploy RFID-based biosensors.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509008988
DOIs
StatePublished - Jun 28 2016
Event2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016 - St. Louis, United States
Duration: May 18 2016May 20 2016

Publication series

Name2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016

Conference

Conference2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016
CountryUnited States
CitySt. Louis
Period5/18/165/20/16

Fingerprint

Radio frequency identification (RFID)
radio
Knit fabrics
monitoring
sensor
Monitoring
Biosensors
antenna
Biofeedback
Antennas
data storage
signal processing
Strain gages
Human engineering
visualization
Learning systems
gauge
Signal processing
Visualization
Health

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Networks and Communications
  • Urban Studies

Cite this

Mongan, W., Anday, E., Dion, G., Fontecchio, A., Joyce, K., Kurzweg, T., ... Dandekar, K. (2016). A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-Based Wireless Wearable Sensors. In 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016 [7501674] (2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMARTCOMP.2016.7501674
Mongan, William ; Anday, Endla ; Dion, Genevieve ; Fontecchio, Adam ; Joyce, Kelly ; Kurzweg, Timothy ; Liu, Yuqiao ; Montgomery, Owen ; Rasheed, Ilhaan ; Sahin, Cem ; Vora, Shrenik ; Dandekar, Kapil. / A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-Based Wireless Wearable Sensors. 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016. Institute of Electrical and Electronics Engineers Inc., 2016. (2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016).
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abstract = "We have applied passive Radio Frequency Identification (RFID), typically used for inventory management, to implement a novel knit fabric strain gauge assembly using conductive thread. As the fabric antenna is stretched, the strength of the received signal varies, yielding potential for wearable, wireless, powerless smart-garment devices based on small and inexpensive passive RFID technology. Knit fabric sensors and other RFID biosensors can enable comfortable, continuous monitoring of biofeedback, but requires an integrated framework consisting of antenna modeling and fabrication, signal processing and machine learning on the noisy wireless signal, secure HIPAA- compliant data storage, visualization and human factors, and integration with existing medical devices and electronic health records (EHR) systems. We present a multidisciplinary, end-to-end framework to study, model, develop, and deploy RFID-based biosensors.",
author = "William Mongan and Endla Anday and Genevieve Dion and Adam Fontecchio and Kelly Joyce and Timothy Kurzweg and Yuqiao Liu and Owen Montgomery and Ilhaan Rasheed and Cem Sahin and Shrenik Vora and Kapil Dandekar",
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Mongan, W, Anday, E, Dion, G, Fontecchio, A, Joyce, K, Kurzweg, T, Liu, Y, Montgomery, O, Rasheed, I, Sahin, C, Vora, S & Dandekar, K 2016, A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-Based Wireless Wearable Sensors. in 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016., 7501674, 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016, St. Louis, United States, 5/18/16. https://doi.org/10.1109/SMARTCOMP.2016.7501674

A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-Based Wireless Wearable Sensors. / Mongan, William; Anday, Endla; Dion, Genevieve; Fontecchio, Adam; Joyce, Kelly; Kurzweg, Timothy; Liu, Yuqiao; Montgomery, Owen; Rasheed, Ilhaan; Sahin, Cem; Vora, Shrenik; Dandekar, Kapil.

2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7501674 (2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016).

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

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AU - Rasheed, Ilhaan

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AU - Vora, Shrenik

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AB - We have applied passive Radio Frequency Identification (RFID), typically used for inventory management, to implement a novel knit fabric strain gauge assembly using conductive thread. As the fabric antenna is stretched, the strength of the received signal varies, yielding potential for wearable, wireless, powerless smart-garment devices based on small and inexpensive passive RFID technology. Knit fabric sensors and other RFID biosensors can enable comfortable, continuous monitoring of biofeedback, but requires an integrated framework consisting of antenna modeling and fabrication, signal processing and machine learning on the noisy wireless signal, secure HIPAA- compliant data storage, visualization and human factors, and integration with existing medical devices and electronic health records (EHR) systems. We present a multidisciplinary, end-to-end framework to study, model, develop, and deploy RFID-based biosensors.

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Mongan W, Anday E, Dion G, Fontecchio A, Joyce K, Kurzweg T et al. A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-Based Wireless Wearable Sensors. In 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7501674. (2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016). https://doi.org/10.1109/SMARTCOMP.2016.7501674