Roland van Wegberg

Bio: Roland van Wegberg is an academic researcher from IMEC. The author has contributed to research in topics: Instrumentation amplifier & Silicon photomultiplier. The author has an hindex of 9, co-authored 19 publications receiving 371 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 769 μW Battery-Powered Single-Chip SoC With BLE for Multi-Modal Vital Sign Monitoring Health Patches

Abstract: An all-in-one battery powered low-power SoC for measuring multiple vital signs with wearables is proposed. All functionality needed in a typical wearable use case scenario, including dedicated readouts, power management circuitry, digital signal processing and wireless communication (BLE) is integrated in a single die. This high level of integration allows an unprecedented level of miniaturization leading to smaller component count which reduces cost and improves comfort and signal integrity. The SoC includes an ECG, Bio-Impedance and a fully differential PPG readout and can interface with external sensors (like an IMU). In a typical application scenario where all sensor readouts are enabled and key features (like heart rate) are calculated on the chip and streamed over the radio, the SoC consumes only 769 μW from the regulated 1.2 V supply.

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).

A 36 μW 1.1 mm 2 Reconfigurable Analog Front-End for Cardiovascular and Respiratory Signals Recording

Abstract: This paper presents a 1.2 V 36 μW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates ${\text{1}}\text{/}f$ noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×–5.3× smaller chip area per channel.

A 665μW silicon photomultiplier-based NIRS/EEG/EIT monitoring asic for wearable functional brain imaging

Abstract: Functional brain imaging is considered a powerful and practical solution for understanding the brain and neurological diseases. While EEG is an established method for non-invasive electrical activity, electrical-impedance tomography (EIT) and near-infrared spectroscopy (NIRS) can additionally measure impedance changes and hemodynamic processes. To facilitate long-term multi-channel brain imaging in a wearable form factor without cabling overhead, there is a need for low-power local amplifiers [1] to support all these modalities. The main principle of optical hemodynamic measurements is to send light pulses into the tissue and measure the reflected light, which is modulated by the oxygen levels in the blood (Fig. 17.8.1). State-of-the-art NIRS ICs typically consume a few mW, primarily for the LEDs to meet the required light sensitivity at the photodiodes (PDs). Silicon photomultipliers (SiPMs) are promising alternatives because they have excellent low-light detection capabilities, speed of response and higher detection efficiency in both visible and near infrared range [2]. Hence, SiPMs allow deeper brain sensing depth and the possibility to sample consistent cerebral regions with larger inter-optode distance. This benefit would significantly reduce the number of NIRS channels and the associated power for a wearable NIRS device. Although SiPMs require a higher bias voltage (∼30V) than PDs, they achieve similar NIRS responses with a few hundred times less LED current. This results in a low-power NIRS ASIC and an overall power-efficient system. Existing optical sensing ICs are not suitable for a SiPM because of its large and variable output current. Trimming-based calibration methods [3] suffer from drift over time. Auto-zeroing by swapping an integrator capacitor [4][5] compensates ambient light at the cost of the integrator's headroom. Apart from ambient light, the dynamic range (DR) of the amplifier is also limited by a large NIRS signal, leading to a power-hungry readout.

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.

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.

Wearable EEG and beyond

Abstract: The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of “wearable EEG” which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal “tattoo” type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.