Julia Pettine

Bio: Julia Pettine is an academic researcher from IMEC. The author has contributed to research in topics: Resonator & Instrumentation amplifier. The author has an hindex of 9, co-authored 12 publications receiving 423 citations.

A 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP

Abstract: This paper presents a MUlti-SEnsor biomedical IC (MUSEIC). It features a high-performance, low-power analog front-end (AFE) and fully integrated DSP. The AFE has three biopotential readouts, one bio-impedance readout, and support for general-purpose analog sensors The biopotential readout channels can handle large differential electrode offsets ( ${\pm} $ 400 mV), achieve high input impedance ( ${>}$ 500 M $\Omega$ ), low noise ( ${ 620 nVrms in 150 Hz), and large CMRR ( ${>}$ 110 dB) without relying on trimming while consuming only 31 $\mu$ W/channel. In addition, fully integrated real-time motion artifact reduction, based on simultaneous electrode-tissue impedance measurement, with feedback to the analog domain is supported. The bio-impedance readout with pseudo-sine current generator achieves a resolution of 9.8 m $\Omega$ / $\surd$ Hz while consuming just 58 $\mu$ W/channel. The DSP has a general purpose ARM Cortex M0 processor and an HW accelerator optimized for energy-efficient execution of various biomedical signal processing algorithms achieving 10 $\times$ or more energy savings in vector multiply-accumulate executions.

A Multi(bio)sensor Acquisition System With Integrated Processor, Power Management, $8 \times 8$ LED Drivers, and Simultaneously Synchronized ECG, BIO-Z, GSR, and Two PPG Readouts

Abstract: A battery-powered multisensor acquisition system with five dedicated channels [electrocardiograph (50 $\mu \text{W}$ ), bioimpedance (46 $\mu \text{W}$ ), galvanic skin response (15 $\mu \text{W}$ ), and 2 $\times $ photoplethysmogram (134 $\mu \text{W}$ ) is presented. It includes an ARM Cortex M0, analog and digital filters, timestamp converter and sample rate converter (SRC), and generic interfaces to support additional sensor modalities. The timestamp module makes precise synchronization between the data streams possible. The SRC module makes the sample rates of data from the internal and external sensor readouts compatible with each other, and is up to a factor 35 more energy efficient compared with a software solution. These modules enable performing accurate and reliable (correlation) techniques. The power management includes two buck converters, an LDO, and eight LED drivers, supporting up to 64 LEDs in an $8 \times 8$ matrix organization. It makes this system the most complete and versatile sensor readout system with state-of-the-art performance (1073 $\mu \text{W}$ with all channels enabled).

18.3 A multi-parameter signal-acquisition SoC for connected personal health applications

Abstract: Connected personal healthcare, or Telehealth, requires smart, miniature wearable devices that can collect and analyze physiological and environmental parameters during a user's daily routine. To truly support emerging applications (Fig. 18.3.1), a generic platform is needed that can acquire a multitude of sensor modalities and has generic energy-efficient signal processing capabilities. SoC technology gives significant advantages for miniaturization. But meeting low-power, medical grade signal quality, multi-sensor support and generic signal processing is still a challenge. For instance, [1] demonstrated a multi-sensor interface but it lacks support for efficient on-chip signal processing and doesn't have a high performance AFE. [2] showed a very low power signal processor but without support for multi-sensor interfacing. [3] presented a highly integrated SoC but lacking power efficiency. This paper demonstrates a highly integrated low-power SoC with enough flexibility to support many emerging applications. A wide range of sensor modalities are supported including 3-lead ECG and bio-impedance via high-performance and low-power AFE. The ARM Cortex™ M0 processor and matrix-multiply-accumulate accelerator can execute numerous biomedical signal processing algorithms (e.g. Independent Component Analysis (ICA), Principal Component Analysis (PCA,) CWT, feature extraction/classification, etc.) in an energy efficient way without sacrificing flexibility. The diversity in supported modalities and the generic processing capabilities, all provided in a single-chip low-power solution, make the proposed SoC a key enabler for emerging personal health applications (Fig. 18.3.1).

A 13 $\mu {\rm A}$ Analog Signal Processing IC for Accurate Recognition of Multiple Intra-Cardiac Signals

Abstract: A low-power analog signal processing IC is presented for the low-power heart rhythm analysis. The ASIC features 3 identical, but independent intra-ECG readout channels each equipping an analog QRS feature extractor for low-power consumption and fast diagnosis of the fatal case. A 16-level digitized sine-wave synthesizer together with a synchronous readout circuit can measure bio-impedance in the range of 0.1-4.4 kΩ with 33 mΩrms resolution and higher than 97% accuracy. The proposed 25 mm2 ASIC consumes only 13 μA from 2.2 V. It is a highly integrated solution offering all the functionality of acquiring multiple high quality intra-cardiac signals, requiring only a few limited numbers of external passives.

28.4 A battery-powered efficient multi-sensor acquisition system with simultaneous ECG, BIO-Z, GSR, and PPG

Abstract: Wearable electronics are becoming mainstream, making personal healthcare devices in the form of, e.g., patches, wristbands and smart watches a reality. However, compared to a clinical setting, these devices have to perform their recordings in an unfavorable manner. User-comfort requirements force the use of dry electrodes without skin preparation and operation during ambulatory conditions, making reliable and sustainable measurements a real challenge. As a consequence, the signal quality and recording reliability are often rather poor. In addition, most devices only focus on a single health parameter (like ECG) resulting in a limited health assessment.

Wearables in Medicine

Abstract: Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches.

Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices

Abstract: Endpoint devices for Internet-of-Things not only need to work under extremely tight power envelope of a few milliwatts, but also need to be flexible in their computing capabilities, from a few kOPS to GOPS. Near-threshold (NT) operation can achieve higher energy efficiency, and the performance scalability can be gained through parallelism. In this paper, we describe the design of an open-source RISC-V processor core specifically designed for NT operation in tightly coupled multicore clusters. We introduce instruction extensions and microarchitectural optimizations to increase the computational density and to minimize the pressure toward the shared-memory hierarchy. For typical data-intensive sensor processing workloads, the proposed core is, on average, $3.5\times $ faster and $3.2\times $ more energy efficient, thanks to a smart L0 buffer to reduce cache access contentions and support for compressed instructions. Single Instruction Multiple Data extensions, such as dot products, and a built-in L0 storage further reduce the shared-memory accesses by $8\times $ reducing contentions by $3.2\times $ . With four NT-optimized cores, the cluster is operational from 0.6 to 1.2 V, achieving a peak efficiency of 67 MOPS/mW in a low-cost 65-nm bulk CMOS technology. In a low-power 28-nm FD-SOI process, a peak efficiency of 193 MOPS/mW (40 MHz and 1 mW) can be achieved.

A 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP

Abstract: This paper presents a MUlti-SEnsor biomedical IC (MUSEIC). It features a high-performance, low-power analog front-end (AFE) and fully integrated DSP. The AFE has three biopotential readouts, one bio-impedance readout, and support for general-purpose analog sensors The biopotential readout channels can handle large differential electrode offsets ( ${\pm} $ 400 mV), achieve high input impedance ( ${>}$ 500 M $\Omega$ ), low noise ( ${ 620 nVrms in 150 Hz), and large CMRR ( ${>}$ 110 dB) without relying on trimming while consuming only 31 $\mu$ W/channel. In addition, fully integrated real-time motion artifact reduction, based on simultaneous electrode-tissue impedance measurement, with feedback to the analog domain is supported. The bio-impedance readout with pseudo-sine current generator achieves a resolution of 9.8 m $\Omega$ / $\surd$ Hz while consuming just 58 $\mu$ W/channel. The DSP has a general purpose ARM Cortex M0 processor and an HW accelerator optimized for energy-efficient execution of various biomedical signal processing algorithms achieving 10 $\times$ or more energy savings in vector multiply-accumulate executions.

RF power harvesting: a review on designing methodologies and applications

Abstract: Wireless power transmission was conceptualized nearly a century ago. Certain achievements made to date have made power harvesting a reality, capable of providing alternative sources of energy. This review provides a summ ary of radio frequency (RF) power harvesting technologies in order to serve as a guide for the design of RF energy harvesting units. Since energy harvesting circuits are designed to operate with relatively small voltages and currents, they rely on state-of-the-art electrical technology for obtaining high efficiency. Thus, comprehensive analysis and discussions of various designs and their tradeoffs are included. Finally, recent applications of RF power harvesting are outlined.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition

Abstract: Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.