A 1 trillion node internet of things (IoT) will require sensing platforms that support numerous applications using power harvesting to avoid the cost and scalability challenge of battery replacement in such large numbers. Our previous SoCs achieve good integration and energy harvesting, but they limit supported applications, need higher end-to-end harvesting efficiency, and require duty-cycling for RF communication. In this project, we demonstrates a highly integrated, flexible SoC platform that supports multiple sensing modalities, extracts information from data flexibly across applications, harvests and delivers power efficiently, and communicates wirelessly.
Body Sensor Networks
|This work presents a tri-modal self-adaptive photoplethysmography (PPG) sensor interface IC for concurrently monitoring heart rate, SpO 2 , and pulse transit time, which is a critical intermediate parameter to derive blood pressure. By implementing a highly-reconfigurable analog frontend (AFE) architecture, flexible signal chain timing control, and flexible dual-LED drivers, this sensor interface provides wide operating space to support various PPG-sensing use cases. A heart-beat-locked-loop (HBLL) scheme is further extended to achieve time-multiplexed dual-input pulse transit time extraction based on two PPG sensors placed at fingertip and chest. A selfadaptive calibration scheme is proposed to automatically match the chip's operating point with the current use case, guaranteeing a sufficient signal-to-noise ratio for the user while consuming minimum system power. This paper proposes a DC offset cancellation (DCOC) approach comprised by a logarithmic transimpedance amplifier and an 8-bit SAR ADC, achieving a measured 38 nA residue error and 8.84 μA maximum input current. Fabricated in a 65nm CMOS process, the proposed trimodal PPG sensor interface consumes 2.3 - 5.7 μW AFE power and 1.52 mm2 die area with 102dB (SpO 2 mode), 110 – 116 dB (HR & PTT mode) dynamic range. A SpO 2 test case and a HR & PTT test case are both demonstrated in the paper, achieving 18.9 μW and 43.7 μW system power, respectively.
This project centers on the development of a custom system-on-chip (SoC)for electrocardiogram (ECG) acquisition and analysis. ECG data is important for medical diagnosis of general health and of many specific conditions, including a variety of cardiac arrhythmias. Existing methods of capturing ECG outside of the clinical setting, such as Holter monitors or event monitors, either have limited lifetimes or non continuous acquisition. This project seeks to develop custom hardware to dramatically extend the lifetime of ECG acquisition circuits by fabricating a custom SoC.
In this project, a low power wireless ECG sensor is implemented using commercial off the shelf (COTS) components. The resulting system can acquire and process ECG data and send it wirelessly to a basestation such as a handheld device. The picture shows the ECG sensor in operation, with the PDA plotting the real time ECG signal.
The Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST) Center is a new NSF Nanosystems Engineering Research Center (NERC) that will develop and employ nano-enabled energy harvesting, energy storage, nanodevices and sensors to create innovative battery-free, body-powered, and wearable health monitoring systems. The four partner schools in the center are NC State, UVA, Penn State, and Florida International University. Our group is leading the integration of the center technologies into a complete electrical system for wearable health and personal environmental monitoring.