Conclusions and Future Work
This chapter summarizes various aspects discussed in detail in this book. Specific attention is paid in highlighting the key contributions—analog and algorithm assisted signal processing architectures for ultra-low-power biosignal acquisition and processing. These aspects are demonstrated through ASIC implementations of adaptive sampling for electrocardiogram (ECG) and compressive sampling for photoplethysmogram (PPG), respectively. This chapter also presents the opportunities to further the work presented in this book in terms of motion artifact reduction in PPG acquisition and combining ultra-low-power ECG and PPG acquisition for cuffless blood pressure (BP) estimation.
- 16.E.S. Winokur, Single-site, noninvasive, blood pressure measurements at the ear using ballistocardiogram (BCG), and photoplethysmogram (PPG), and a low-power, reflectance-mode PPG SoC, Ph.D. dissertation, Massachusetts Institute of Technology, 2014Google Scholar
- 18.C. Poon, Y. Zhang, Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time, in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (2005)Google Scholar
- 112.V.R. Pamula, M. Verhelst, System and method for heart rate detection with motion artifact reduction, U.S. Patent App. 15/939,073, 4 Oct 2018Google Scholar
- 113.V.R. Pamula, C. Van Hoof, M. Verhelst, An ultra-low power, robust photoplethysmographic readout exploiting compressive sampling, artifact reduction, and sensor fusion, in Hybrid ADCs, Smart Sensors for the IoT, and Sub-1V & Advanced Node Analog Circuit Design (Springer, Cham, 2018), pp. 145–163Google Scholar
- 115.P.K. Baheti, H. Garudadri, An ultra low power pulse oximeter sensor based on compressed sensing, in 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (Jun 2009)Google Scholar
- 116.V.R. Pamula, M. Verhelst, System and method for cuffless blood pressure estimation, Oct. 4 2018. US Patent App. 15/939,119Google Scholar