This book discusses the design and implementation aspects of ultra-low power biosignal acquisition platforms that exploit analog-assisted and algorithmic approaches for power savings.The authors describe an approach referred to as analog-and-algorithm-assisted signal processing.This enables significant power consumption reductions by implementing low power biosignal acquisition systems, leveraging analog preprocessing and algorithmic approaches to reduce the data rate very early in the signal processing chain.They demonstrate savings for wearable sensor networks (WSN) and body area networks (BAN), in the sensors' stimulation power consumption, as well in the power consumption of the digital signal processing and the radio link. Two specific implementations, an adaptive sampling electrocardiogram (ECG) acquisition and a compressive sampling (CS) photoplethysmogram (PPG) acquisition system, are demonstrated.

• First book to present the so called, analog-and-algorithm-assisted approaches for ultra-low power biosignal acquisition and processing platforms;

• Covers the recent trend of beyond Nyquist rate signal acquisition and processing in detail, including adaptive sampling and compressive sampling paradigms;

• Includes chapters on compressed domain feature extraction, as well as acquisition of photoplethysmogram, an emerging optical sensing modality, including compressive sampling based PPG readout with embedded feature extraction;

• Discusses emerging trends in sensor fusion for improving the signal integrity, as well as lowering the power consumption of biosignal acquisition systems.

First book to present the so called, analog- and algorithm-assisted approaches for ultra-low power biosignal acquisition and processing platforms Covers the recent trend of beyond Nyquist rate signal acquisition and processing in detail, including adaptive sampling and compressive sampling paradigms Includes chapters on compressed domain feature extraction, as well as acquisition of photoplethysmogram, an emerging optical sensing modality, including compressive sampling based PPG readout with embedded feature extraction Discusses emerging trends in sensor fusion for improving the signal integrity, as well as lowering the power consumption of biosignal acquisition systems

Autorentext
Venkata Rajesh Pamula received in BTech in electrical engineering in 2007 from the Indian Institute of Technology and Master of science from Imperial College London in 2010. He worked in various capacities with the Samsung Semiconductors between 2007-09 and 2010-12 before starting to work towards his PhD at KU Leuven in association with imec Belgium. He is currently a visiting scientist with the department of electrical engineering, University of Washington Seattle. His major areas of research are ultra low power sensor readouts, energy efficient mixed signal circuits and systems.

Mr. Pamula is a recipient of the Government of Andhra Pradesh Gold Medal in 2003, ISSCC analog devices outstanding student designer award in 2016 and also received four gold medals from IIT in 2007 for outstanding academic performance.

Chris Van Hoof is Senior Director of Connected Health Solutions at imec in Leuven, Belgium and Eindhoven, the Netherlands and imec Fellow. He is also full professor (part-time) in the Electrical Engineering Department of KU Leuven. After a PhD in Electrical Engineering (University of Leuven, 1992), Chris Van Hoof has held positions at imec at manager and director level in diverse technical fields (sensors and imagers, MEMS and autonomous microsystems, wireless sensors, patient monitoring solutions, medical devices, behavioral technology). He has published over 700 papers in journals and conference proceedings in those fields and has given over 100 invited talks. His R&D has resulted in 5 startups (4 in the healthcare domain) and he has delivered flight hardware to 2 European Space Agency missions.

Prof. Dr. ir. Marian Verhelst is a professor at the MICAS laboratories (MICro-electronics And Sensors) of the Electrical Engineering Department of KU Leuven. Her research focuses on embedded machine learning, energy-efficient hardware accelerators, self-adaptive circuits and systems, and low-power sensing and processing. Before that, she received a PhD from KU Leuven cum ultima laude, she was a visiting scholar at the Berkeley Wireless Research Center (BWRC) of UC Berkeley, and she worked as a research scientist at Intel Labs, Hillsboro OR. Prof. Verhelst is an SSCS Distinguished Lecturer and a member of the DATE conference executive committee. She held various roles in the ESSCIRC and ISSCC TPCs, the ISSCC executive committee, the Young Academy of Belgium, editorial board of TCAS-II and JSSC, and the STEM advisory commitee to the Flemish Government. Marian holds a prestigious ERC Grant from the European Union.

Inhalt
Chapter1: Challenges and Opportunities in Wearable Biomedical Interfaces.- Chapter2: Adaptive Sampling for Ultra-low Power Electrocardiogram (ECG) Readouts.- Chapter3: Introduction to Compressive Sampling (CS).- Chapter4: Compressed Domain Feature Extraction.- Chapter5: A Low Power Compressive Sampling (CS) Photoplethysmogram (PPG) Read-out With Embedded Feature Extraction.- Chapter6: Conclusions and Future Work. <p