Wearable ECG SoC for Wireless Body Area Networks: Implementation with Fuzzy Decision Making Chip

International audienceThe work aims to present an ultra-low power Electrocardiogram (ECG) on a chip with an integrated Fuzzy Decision Making (FDM) chip for Wireless Body Sensor Networks (WBSN) applications. The developed device is portable, wearable, long battery life, and small in size. The device comprises two designed chips, ECG System-on-Chip and Fuzzy Decision Maker chip. The ECG on-chip contains an analog front end circuit and a 12-bit SAR ADC for signal conditioning, a QRS detector, and relevant control circuitry and interfaces for processing. The analog ECG front-end circuits precisely measure and digitize the raw ECG signal. The QRS complex with a sampling frequency of 256 Hz is extracted after filtering. The extracted QRS details are sent to the decision maker chip, where abnormalities/anomalies in patient’s health are detected and an alert signal is sent to the patient via wireless communication protocol. The patient’s ECG data is wirelessly transmitted to a PC, using ZigBee or a mobile phone. The chip is prototyped and employed in a standard 0.35 µm CMOS process. The operating voltage of Static RAM and digital circuits and analog core circuits are 3.3 V and 1 V, respectively. The total area of the device is about 6 $$\text{cm}^{\text{2}}$$ and consumes about 8.5 µW. Small size and low power consumption show the effectiveness of the proposed design, suitable for wireless wearable ECG monitoring devices

PHIDIAS: ultra-low-power holistic design for smart bio-signals computing platforms

Emerging and future HealthCare policies are fueling up an application-driven shift toward long-Term monitoring of biosignals by means of embedded ultra-low power Wireless Body Sensor Networks (WBSNs). In order to break out, these applications needed the emergence of new technologies to allow the development of extremely power-efficient bio-sensing nodes. The PHIDIAS project aims at unlocking the development of ultra-low power bio-sensing WBSNs by tackling multiple and interlocking technological breakthroughs: (i) the development of new signal processing models and methods based on the recently proposed Compressive Sampling paradigm, which allows the design of energy-minimal computational architectures and analog front-ends, (ii) the efficient hardware implementation of components, both analog and digital, building upon an innovative ultra-low-power signal processing front-end, (iii) the evaluation of the global power reduction using a system wide integration of hardware and software components focused on compressed-sensingbased bio-signals analysis. PHIDIAS brought together a mixed consortium of academic and industrial research partners representing pan-European excellence in different fields impacting the energy-Aware optimization of WBSNs, including experts in signal processing and digital/analog IC design. In this way, PHIDIAS pioneered a unique holistic approach, ensuring that key breakthroughs worked out in a cooperative way toward the global objective of the project