Mario Konijnenburg

Bio: Mario Konijnenburg is an academic researcher from IMEC. The author has contributed to research in topics: Computer science & Automatic test pattern generation. The author has an hindex of 23, co-authored 58 publications receiving 1808 citations. Previous affiliations of Mario Konijnenburg include Katholieke Universiteit Leuven & Philips.

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 Configurable and Low-Power Mixed Signal SoC for Portable ECG Monitoring Applications

Abstract: This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts. A custom digital signal processor consisting of 4-way SIMD processor provides the configurability and advanced functionality like motion artifact removal and R peak detection. A built-in 12-bit analog-to-digital converter (ADC) is capable of adaptive sampling achieving a compression ratio of up to 7, and loop buffer integration reduces the power consumption for on-chip memory access. The SoC is implemented in 0.18 $\mu$ m CMOS process and consumes 32 $\mu$ W from a 1.2 V while heart beat detection application is running, and integrated in a wireless ECG monitoring system with Bluetooth protocol. Thanks to the ECG SoC, the overall system power consumption can be reduced significantly.

CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment

Abstract: Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram, suffers from motion artifacts inherent in daily life. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only a wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-term memory layers, followed by a dense output layer for modeling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: regression layer—having a single neuron to predict HR; classification layer—two neurons that identify a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47 ± 3.37 beats per minute for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

A structured and scalable test access architecture for TSV-based 3D stacked ICs

Abstract: New process technology developments enable the creation of three-dimensional stacked ICs (3D-SICs) interconnected by means of Through-Silicon Vias (TSVs). This paper presents a DfT test access architecture for such 3D-SICs that allows for both pre-bond die testing and post-bond stack testing. The DfT architecture is based on a modular test approach, in which the various dies, their embedded IP cores, the inter-die TSV-based interconnects, and the external I/Os can be tested as separate units to allow optimization of the 3D-SIC test flow. The architecture builds on and reuses existing DfT hardware at the core, die, and product level. It adds a die-level wrapper, which is based on IEEE 1500, with the following novel features: (1) dedicated probe pads on the non-bottom dies to facilitate pre-bond die testing, (2) TestElevators that transport test control and data signals up and down during post-bond stack testing, and (3) a hierarchical Wrapper Instruction Register (WIR) chain. The paper also hints at opportunities for optimization and standardization of this architecture.

A 2.4 GHz ULP OOK Single-Chip Transceiver for Healthcare Applications

Abstract: This paper describes an ultra-low power (ULP) single chip transceiver for wireless body area network (WBAN) applications. It supports on-off keying (OOK) modulation, and it operates in the 2.36-2.4 GHz medical BAN and 2.4-2.485 GHz ISM bands. It is implemented in 90 nm CMOS technology. The direct modulated transmitter transmits OOK signal with 0 dBm peak power, and it consumes 2.59 mW with 50% OOK. The transmitter front-end supports up to 10 Mbps. The transmitter digital baseband enables digital pulse-shaping to improve spectrum efficiency. The super-regenerative receiver front-end supports up to 5 Mbps with -75 dBm sensitivity. Including the digital part, the receiver consumes 715 μW at 1 Mbps data rate, oversampled at 3 MHz. At the system level the transceiver achieves PER=10 -2 at 25 meters line of site with 62.5 kbps data rate and 288 bits packet size. The transceiver is integrated in an electrocardiogram (ECG) necklace to monitor the heart's electrical property.

An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

Abstract: Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases

Ultra-Low Power VLSI Circuit Design Demystified and Explained: A Tutorial

Abstract: In this paper, the state of the art in ultra-low power (ULP) VLSI design is presented within a unitary framework for the first time. A few general principles are first introduced to gain an insight into the design issues and the approaches that are specific to ULP systems, as well as to better understand the challenges that have to be faced in the foreseeable future. Intuitive understanding is accompanied by rigorous analysis for each key concept. The analysis ranges from the circuit to the micro-architectural level, and reference is given to process, physical and system levels when necessary. Among the main goals of this paper, it is shown that many paradigms and approaches borrowed from traditional above-threshold low-power VLSI design are actually incorrect. Accordingly, common misconceptions in the ULP domain are debunked and replaced with technically sound explanations.

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.