992 research outputs found

    Ultra-low-power ECG front-end design based on compressed sensing

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    Ultra-low-power design has been a challenging area for design of the sensor front-ends especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power budget and hardware resources are available. Since introduction of Compressed Sensing, there has been a challenge to design CS-based low-power readout devices for different applications and among all for biomedical signals. Till now, different proposed realizations of the digital CS prove the suitability of using CS as an efficient low-power compression technique for compressible biomedical signals. However, these works mainly take advantages of only one aspect of the benefits of the CS. In this type of works, CS is usually used as a very low cost and easy to implement compression technique. This means that we should acquire the signal with traditional limitations on the bandwidth (BW) and later compresses it. However, the main power of the CS, which lies on the efficient data acquisition, remains untouched. Building on our previous work [1], where the suitability of the CS is proven for the compression of the ECG signals, and our investigation on ultra-low-power CS-based A2I devices [2] , here in this paper we propose a fully redesigned complete CS-based “Analog-to-information” (A/I) front-end for ECG signals. Our results show that proposed hybrid design easily outperforms the traditional implementation of CS with more than 11 times fold reduction in power consumption compared to standard implementation of CS. Moreover our design shows a very promising performance specially in high compression ratio

    An architecture for ultra-low-voltage ultra-low-power compressed sensing-based acquisition systems

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    Compressed Sensing (CS) has been addressed as a paradigm capable of lowering energy requirements in acquisition systems. Furthermore, the capability of simultaneously acquiring and compressing an input signal makes this paradigm perfectly suitable for low-power devices. However, the need for analog hardware blocks makes the adoption of most of standard solutions proposed so far in the literature problematic when an aggressive voltage and energy scaling is considered, as in the case of ultra-low-power IoT devices that need to be battery-powered or energy harvesting-powered. Here, we investigate a recently proposed architecture that, due to the lack of any analog block (except for the comparator required in the following A/D stage) is compatible with the aggressive voltage scaling required by IoT devices. Feasibility and expected performance of this architecture are investigated according to the most recent state-of-the-art literature

    Robust Algorithms for Unattended Monitoring of Cardiovascular Health

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    Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring

    Communication channel analysis and real time compressed sensing for high density neural recording devices

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    Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

    Bioelectronic Sensor Nodes for Internet of Bodies

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    Energy-efficient sensing with Physically-secure communication for bio-sensors on, around and within the Human Body is a major area of research today for development of low-cost healthcare, enabling continuous monitoring and/or secure, perpetual operation. These devices, when used as a network of nodes form the Internet of Bodies (IoB), which poses certain challenges including stringent resource constraints (power/area/computation/memory), simultaneous sensing and communication, and security vulnerabilities as evidenced by the DHS and FDA advisories. One other major challenge is to find an efficient on-body energy harvesting method to support the sensing, communication, and security sub-modules. Due to the limitations in the harvested amount of energy, we require reduction of energy consumed per unit information, making the use of in-sensor analytics/processing imperative. In this paper, we review the challenges and opportunities in low-power sensing, processing and communication, with possible powering modalities for future bio-sensor nodes. Specifically, we analyze, compare and contrast (a) different sensing mechanisms such as voltage/current domain vs time-domain, (b) low-power, secure communication modalities including wireless techniques and human-body communication, and (c) different powering techniques for both wearable devices and implants.Comment: 30 pages, 5 Figures. This is a pre-print version of the article which has been accepted for Publication in Volume 25 of the Annual Review of Biomedical Engineering (2023). Only Personal Use is Permitte

    Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

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    This paper presents the system-level architecture of novel ultra-low power wireless body sensor nodes (WBSNs) for real-time cardiac monitoring and analysis, and discusses the main design challenges of this new generation of medical devices. In particular, it highlights first the unsustainable energy cost incurred by the straightforward wireless streaming of raw data to external analysis servers. Then, it introduces the need for new cross-layered design methods (beyond hardware and software boundaries) to enhance the autonomy of WBSNs for ambulatory monitoring. In fact, by embedding more onboard intelligence and exploiting electrocardiogram (ECG) specific knowledge, it is possible to perform real-time compressive sensing, filtering, delineation and classification of heartbeats, while dramatically extending the battery lifetime of cardiac monitoring systems. The paper concludes by showing the results of this new approach to design ultra-low power wearable WBSNs in a real-life platform commercialized by SmartCardia. This wearable system allows a wide range of applications, including multi-lead ECG arrhythmia detection and autonomous sleep monitoring for critical scenarios, such as monitoring of the sleep state of airline pilot

    Adapted Compressed Sensing: A Game Worth Playing

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    Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing

    Hardware-Software Inexactness in Noise-aware Design of Low-Power Body Sensor Nodes

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    Wireless Body Sensor Nodes (WBSNs) are miniaturized and ultra-low-power devices, able to acquire and wirelessly trans- mit biosignals such as electrocardiograms (ECG) for extended periods of times and with little discomfort for subjects [1]. Energy efficiency is of paramount importance for WBSNs, because it allows a higher wearability (by requiring a smaller battery) and/or an increased mean time between charges. In this paper, we investigate how noise-aware design choices can be made to minimize energy consumption in WBSNs. Noise is unavoidable in biosignals acquisitions, either due to external factors (in case of ECGs, muscle contractions and respiration of subjects [2]) or to the design of the front- end analog acquisition block. From this observation stems the opportunity to apply inexact strategies such as on-node lossy compression to minimize the bandwidth over the energy- hungry wireless link [3], as long as the output quality of the signal, when reconstructed on the receiver side, is not constrained by the performed compression. To maximize gains, ultra-low-power platforms must be employed to perform the above-mentioned Digital Signal Processing (DSP) techniques. To this end, we propose an under-designed (but extremely efficient) architecture that only guarantees the correctness of operations performed on the most significant data (i.e., data most affecting the final results), while allowing sporadic errors for the less significant data

    Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

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    The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications
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