Abstract
To optimize energy consumption in wearable sensor networks, an efficient scheme is to set the sensors in sleep mode and wake them up to engage communication. However, synchronicity between the sensors needs to be assured by always-on local oscillators. This work proposes a different topology that takes advantage of the heart beat to wake-up wearable sensors. The electrocardiogram (ECG) is detected by two probes and then converted into a pulse signal. Using 28-nm FD-SOI CMOS technology, this solution is implemented on a circuit consuming 19 nW at a 900 mV supply voltage, hence suitable for long term and wearable applications.
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References
Benarrouch, R., et al.: Heartbeat-based synchronization scheme for the human intranet: modeling and analysis (2020). http://arxiv.org/abs/2005.05915
Berwal, D., Vandana, C., Dewan, S., Jiji, C., Baghini, M.: Motion artifact removal in ambulatory ECG signal for heart rate variability analysis. IEEE Sens. J. 19(24), 12432–12442 (2019). https://doi.org/10.1109/JSEN.2019.2939391
Bose, S., Shen, B., Johnston, M.L.: A 20 \(\upmu \)W heartbeat detection system-on-chip powered by human body heat for self-sustaining wearable healthcare. In: 2020 IEEE International Solid-State Circuits Conference - (ISSCC), pp. 408–410 (2020). https://doi.org/10.1109/ISSCC19947.2020.9063071
Chen, Y., et al.: An injectable 64 nW ECG mixed-signal SoC in 65 nm for arrhythmia monitoring. IEEE J. Solid State Circuits 50(1), 375–390 (2015). https://doi.org/10.1109/JSSC.2014.2364036
Dong, Y., et al.: A 1.8 \(\upmu \)W 32 nV/\(\sqrt{{\rm H}}\)z current-reuse capacitively-coupled instrumentation amplifier for EEG detection. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4, May 2017. https://doi.org/10.1109/ISCAS.2017.8050494
Goldberger, A., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 23(101), e215–e220 (2003). https://doi.org/10.1161/01.CIR.101.23.e215
Ha, H., et al.: A bio-impedance readout IC with digital-assisted baseline cancellation for two-electrode measurement. IEEE J. Solid State Circuits 54(11), 2969–2979 (2019). https://doi.org/10.1109/JSSC.2019.2939077
He, D.D., Sodini, C.G.: A 58 nW ECG ASIC with motion-tolerant heartbeat timing extraction for wearable cardiovascular monitoring. IEEE Trans. Biomed. Circuits Syst. 9(3), 370–376 (2015). https://doi.org/10.1109/TBCAS.2014.2346761
Li, H., Tan, J.: Heartbeat-driven medium-access control for body sensor networks. IEEE Trans. Inf. Technol. Biomed. 14(1), 44–51 (2010). https://doi.org/10.1109/TITB.2009.2028136
Izumi, S., et al.: Normally off ECG SoC with non-volatile MCU and noise tolerant heartbeat detector. IEEE Trans. Biomed. Circuits Syst. 9(5), 641–651 (2015). https://doi.org/10.1109/TBCAS.2015.2452906
Larras, B., Frappé, A.: On the distribution of clique-based neural networks for edge AI. IEEE J. Emerg. Sel. Top. Circuits Syst. 1 (2020). https://doi.org/10.1109/JETCAS.2020.3023481
Laurijssen, D., Saeys, W., Truijen, S., Daems, W., Steckel, J.: Synchronous wireless body sensor network enabling human body pose estimation. IEEE Access 7, 49341–49351 (2019). https://doi.org/10.1109/ACCESS.2019.2910636
McSharry, P., Clifford, A., Tarassenko, L., Smith, L.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 3(50), 289–294 (2003). https://doi.org/10.1109/TBME.2003.808805
Xiang, J., Dong, Y., Xue, X., Xiong, H.: Electronics of a wearable ECG with level crossing sampling and human body communication. IEEE Trans. Biomed. Circuits Syst. 13(1), 68–79 (2019). https://doi.org/10.1109/TBCAS.2018.2879818
Yan, L., et al.: A 680nA ECG acquisition IC for leadless pacemaker applications. IEEE Trans. Biomed. Circuits Syst. 8(6), 779–786 (2014). https://doi.org/10.1109/TBCAS.2014.2377073
Yoo, J., Yan, L., Lee, S., Kim, Y., Yoo, H.: A 5.2 mW self-configured wearable body sensor network controller and a 12\(\upmu \)W wirelessly powered sensor for a continuous health monitoring system. IEEE J. Solid State Circuits 45(1), 178–188 (2010). https://doi.org/10.1109/JSSC.2009.2034440
Zhang, J., Zhang, H., Sun, Q., Zhang, R.: A low-noise, low-power amplifier with current-reused OTA for ECG recordings. IEEE Trans. Biomed. Circuits Syst. 12(3), 700–708 (2018). https://doi.org/10.1109/TBCAS.2018.2819207
Acknowledgment
This work has been initiated in the frame of the Human Body Intranet collaborative project between STMicroelectronics, BWRC at UC Berkeley and IEMN. The authors wish to acknowledge Andreia Cathelin, Jan Rabaey and Andreas Kaiser for their support. This work was supported in part by the European CHIST-ERA JEDAI program under number ANR-19-CHR3-0005-01.
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Gautier, A., Dael, M., Benarrouch, R., Larras, B., Frappé, A. (2020). An Ultra-low-Power Integrated Heartbeat Detector for Wearable Sensors. In: Alam, M.M., Hämäläinen, M., Mucchi, L., Niazi, I.K., Le Moullec, Y. (eds) Body Area Networks. Smart IoT and Big Data for Intelligent Health. BODYNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-030-64991-3_14
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