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

doi:10.1109/SMARTCOMP.2016.7501674

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

School of Engineering (Behrend)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Scopus citations

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 language	English (US)
Title of host publication	2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509008988
DOIs	https://doi.org/10.1109/SMARTCOMP.2016.7501674
State	Published - Jun 28 2016
Event	2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016 - St. Louis, United States Duration: May 18 2016 → May 20 2016

Publication series

Name	2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016

Conference

Conference	2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016
Country/Territory	United States
City	St. Louis
Period	5/18/16 → 5/20/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Computer Networks and Communications
Urban Studies

Access to Document

10.1109/SMARTCOMP.2016.7501674

Cite this

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 Article 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 et al. / 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).

@inproceedings{395d5971db14400dbe37d0f28fe2a712,

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

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",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016 ; Conference date: 18-05-2016 Through 20-05-2016",

year = "2016",

month = jun,

day = "28",

doi = "10.1109/SMARTCOMP.2016.7501674",

language = "English (US)",

series = "2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016",

address = "United States",

}

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 et al.
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 proceeding › Conference contribution

TY - GEN

T1 - A Multi-Disciplinary Framework for Continuous Biomedical Monitoring Using Low-Power Passive RFID-Based Wireless Wearable Sensors

AU - Mongan, William

AU - Anday, Endla

AU - Dion, Genevieve

AU - Fontecchio, Adam

AU - Joyce, Kelly

AU - Kurzweg, Timothy

AU - Liu, Yuqiao

AU - Montgomery, Owen

AU - Rasheed, Ilhaan

AU - Sahin, Cem

AU - Vora, Shrenik

AU - Dandekar, Kapil

PY - 2016/6/28

Y1 - 2016/6/28

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/84979536483

UR - https://www.scopus.com/pages/publications/84979536483#tab=citedBy

U2 - 10.1109/SMARTCOMP.2016.7501674

DO - 10.1109/SMARTCOMP.2016.7501674

M3 - Conference contribution

AN - SCOPUS:84979536483

T3 - 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016

BT - 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016

Y2 - 18 May 2016 through 20 May 2016

ER -

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). doi: 10.1109/SMARTCOMP.2016.7501674

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

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this