Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag

Authors

  • Nhat Minh Tran Department of Telecommunication, Sai Gon University, Vietnam
  • Ngoc-Giao Pham Department of Computing Fundamentals, FPT University, Hanoi, Vietnam
  • Thang Viet Tran Department of Science and Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

DOI:

https://doi.org/10.30564/jeis.v4i2.5225

Abstract

This paper proposes a batteryless sensing and computational device to collect and process electrocardiography (ECG) signals for monitoring heart rate variability (HRV). The proposed system comprises of a passive UHF radio frequency identification (RFID) tag, an extreme low power microcontroller, a low-power ECG circuit, and a radio frequency (RF) energy harvester. The microcontroller and ECG circuits consume less power of only ~30 µA and ~3 mA, respectively. Therefore, the proposed RF harvester operating at frequency band of 902 MHz ~ 928 MHz can sufficiently collect available energy from the RFID reader to supply power to the system within a maximum distance of ~2 m. To extract R-peak of the ECG signal, a robust algorithm that consumes less time processing is also developed. The information of R-peaks is stored into an Electronic Product Code (EPC) Class 1st Generation 1st compliant ID of the tag and read by the reader. This reader is functioned to collected the R-peak data with sampling rate of 100ms; therefore, the user application can monitor fully range of HRV. The performance of the proposed system shows that this study can provide a good solution in paving the way to new classes of healthcare applications.

Keywords:

Batteryless ECG sensor, Heart rate monitoring, UHF RFID

References

[1] Amini, N., Xu, W., Li, Z., et al., 2011. Experimental Analysis of IEEE 802. 15. 4 for On / Off Body Com-munications. pp. 2138-2142.

[2] Choi, J.S., 2010. Performance analysis of Zig-Bee-based body sensor networks. 2010 IEEE Interna-tional Conference on Systems, Man and Cybernetics. pp. 2427-2433.

[3] Dementyev, A., Hodges, S., Taylor, S., et al., 2013. Power consumption analysis of Bluetooth Low En-ergy, ZigBee and ANT sensor nodes in a cyclic sleep scenario. Wireless Symposium (IWS), 2013 IEEE International. pp. 2-5.

[4] Andreozzi, E., Sabbadini, R., Centracchio, J., et al., 2022. Multimodal Finger Pulse Wave Sensing: Com-parison of Forcecardiography and Photoplethysmog-raphy Sensors. Sensors. 22, 1-18.

[5] Jung, S.J., Chung, W.Y., 2011. Flexible and Scalable Patient’s Health Monitoring System in 6LoWPAN. Sensor Letters. 9(2), 778-785.

[6] Choi, Y., Zhang, Q., Ko, S., 2013. Noninvasive cuf-fless blood pressure estimation using pulse transit time and Hilbert–Huang transform. Computers & Electrical Engineering. 39(1), 103-111.

[7] Karmakar, C., Khandoker, A., Penzel, T., et al., 2014. Detection of Respiratory Arousals Using Photopleth-ysmography (PPG) Signal in Sleep Apnea Patients. IEEE Biomedical and Health Informatics. 18(3), 1065-1073.

[8] Tran, T.V., Chung, W.Y., 2017. A Robust Algorithm for Peak Detection of Photoplethysmograms Wave-forms in Mobile Devices. Journal of Medical Imag-ing and Health Informatics 7(7), 1617-1623.

[9] Alliance, S.B., 1970. Attachment D : RFID Technol-ogy Overview. pp. 1-13.

[10] Salmeron, J.F., Molina-Lopez, F., Rivadeneyra, A., et al., 2014. Design and Development of Sensing RFID Tags on Flexible Foil Compatible With EPC Gen 2. IEEE Sensors Journal. 14(12), 4361-4371.

[11] Dobkin, D., 2007. The RF in RFID: passive UHF RFID in practice. 1.

[12] RFID standard http://www.scansource.eu/en/educa-tion.htm?eid=12&elang=en.

[13] Liao, Y.J., Na, R.T., Rayside, D., 2014. Accurate ECG R-Peak Detection for Telemedicine.

[14] Rakshita, M., Das, S., 2017. An efficient wave-let-based automated R-peaks detection method using Hilbert transform. Biocybernetics and Biomedical Engineering. 37(3), 566-576.

[15] Chanwimalueang, T., Von Rosenberg, W., Mandic, D.P., 2015. Enabling R-peak Detection in Wearable ECG : Combining Matched Filtering and Hilbert Transform. pp. 134-138.

[16] Rooijakkers, M., Rabotti, C., Bennebroek, M., et al., 2011. Low-complexity R-peak detection in ECG signals: A preliminary step towards ambulato-ry fetal monitoring. International Conference of the IEEE Engineering.

[17] Tran, T., Chung, W.Y., 2015. A Robust Algorithm for Real-Time Peak Detection of Photoplethysmograms using a Personal Computer Mouse. IEEE Sensors Journal. 15(c), 1.

[18] Paper, S., 2009. An Overview of RFID Technology, Application, and Security / Privacy Threats and Solu-tions.

[19] Abdulhadi, A., Abhari, R., 2015. Multi-Port UHF RFID Tag Antenna for Enhanced Energy Harvesting of Self-Powered Wireless Sensors. IEEE Transac-tions on Industrial Informatics. 3203(c), 1.

[20] Rahman, F., Williams, D., Ahamed, S.I., et al., 2014. PriDaC: Privacy Preserving Data Collection in Sen-sor Enabled RFID Based Healthcare Services. IEEE International Symposium on High-assurance Systems Engineering. pp. 236-242.

[21] A. AMS company, 2014. Data Sheet of the SL900A. pp. 1-90.

[22] A. AMS company, 2014. RF Characteristics of The SL900A. pp. 1-9.

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How to Cite

Tran, N. M., Pham, N.-G., & Tran, T. V. (2022). Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag. Journal of Electronic & Information Systems, 4(2), 10–20. https://doi.org/10.30564/jeis.v4i2.5225