An Asynchronous Multi-Sensor Micro Control Unit for Wireless Body Sensor Networks (WBSNs)

In this work, an asynchronous multi-sensor micro control unit (MCU) core is proposed for wireless body sensor networks (WBSNs). It consists of asynchronous interfaces, a power management unit, a multi-sensor controller, a data encoder (DE), and an error correct coder (ECC). To improve the system performance and expansion abilities, the asynchronous interface is created for handshaking different clock domains between ADC and RF with MCU. To increase the use time of the WBSN system, a power management technique is developed for reducing power consumption. In addition, the multi-sensor controller is designed for detecting various biomedical signals. To prevent loss error from wireless transmission, use of an error correct coding technique is important in biomedical applications. The data encoder is added for lossless compression of various biomedical signals with a compression ratio of almost three. This design is successfully tested on a FPGA board. The VLSI architecture of this work contains 2.68-K gate counts and consumes power 496-μW at 133-MHz processing rate by using TSMC 0.13-μm CMOS process. Compared with the previous techniques, this work offers higher performance, more functions, and lower hardware cost than other micro controller designs

Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health Informatic

Model Based Compressed Sensing Reconstruction Algorithms for ECG Telemonitoring in WBANs

Wireless Body area networks (WBANs) consist of sensors that continuously monitor and transmit real time vital signals to a nearby coordinator and then to a remote terminal via the Internet. One of the most important signals for monitoring in WBANs is the electrocardiography (ECG) signal. The design of an accurate and energy efficient ECG telemonitoring system can be achieved by: i) reducing the amount of data that should be transmitted ii) minimizing the computational operations executed at any transmitter/receiver in a WBAN. To this end, compressed sensing (CS) approaches can offer a viable solution. In this paper, we propose two novel CS based ECG reconstruction algorithms that minimize the samples that are required to be transmitted for an accurate reconstruction, by exploiting the block structure of the ECG in the time domain (TD) and in an uncorrelated domain (UD). The proposed schemes require the solutions of second-order cone programming (SOCP) problems that are usually tackled by computational demanding interior point (IP) methods. To solve these problems efficiently, we develop a path-wise coordinate descent based scheme. The reconstruction accuracy is evaluated by the percentage root-mean-square difference (PRD) metric. A reconstructed signal is acceptable if and only if PRD<9%PRD<9%. Simulation studies carried out with real electrocardiographic (ECG) data, show that the proposed schemes, operating in both the TD and in the UD as compared to the conventional CS techniques, reduce the Compression Ratio (CR) by 20%20% and 44%44% respectively, offering at the same time significantly low computational complexity

A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT

IEEE Transactions on Biomedical Circuits and SystemsPP991-1

A Real-Time Compressed Sensing-Based Personal Electrocardiogram Monitoring System

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric tele-cardiology systems. A WBSN-enabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the tele-health provider. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be significantly improved through embedded ECG compression, which reduces airtime over energy-hungry wireless links. In this paper, we propose a novel real-time energy-aware ECG monitoring system based on the emerging compressed sensing (CS) signal acquisition/compression paradigm for WBSN applications. For the first time, CS is demonstrated as an advantageous real-time and energy-efficient ECG compression technique, with a computationally light ECG encoder on the state-of-the-art ShimmerTM wearable sensor node and a realtime decoder running on an iPhone (acting as a WBSN coordinator). Interestingly, our results show an average CPU usage of less than 5% on the node, and of less than 30% on the iPhone

Fatigue and drowsiness detection using inertial sensors and electrocardiogram

Dissertação realizada no âmbito de trabalho final de mestrado para a obtenção de grau de mestre em Engenharia Electrónica e TelecomunicaçõesThe interest in monitoring a driver’s performance has increased in the past years in order to make the roads safer both for drivers and pedestrians. With this thinking in mind, it arises the idea of developing a system to monitor driver’s fatigue and drowsiness to alert him, if needed, about his psychological and physical states. This dissertation is based on the CardioWheel system, developed by CardioID, and consists in monitoring the person’s ECG signal and to record the motion of the steering wheel during the journey. The ECG signal is extracted with dry-electrodes placed in a conductive leather covering the steering wheel that can sense the electrical signal caused by the heartbeat of the person while having the hands on the wheel. The steering wheel movement monitoring is performed with the help of a three-axis accelerometer placed in the middle of the steering wheel that records the proper acceleration variations while moving the steering wheel. With those accelerations it is possible to calculate the steering wheel rotation angle during all the journey. The amount of data acquired with this system undergoes a compression stage for transmission with the goal of reducing the necessary bandwidth. From the evaluated techniques for data compression, it was possible to conclude that the hybrid method using Linear Predictive Coding and Lempel-Ziv-Welch is the lossless technique with the highest Compression Ratio. However, the hybrid technique using amplitude scaling e DWT is the lossy method with the highest Compression Ratio and a reduced RMSE. The transmission of the compressed data is done via Bluetooth® Low Energy, available in the CardioWheel system, with an exclusive profile developed for this dissertation. This profile has the ability to transmit the ECG and accelerometer data in real time. To detect if the driver is becoming drowsy, were evaluated machine learning algorithms to detect fatigue and drowsiness patterns according to the received ECG and accelerometer data from the steering wheel. Many features were extracted to describe the main characteristics from both signals and, from all the tested techniques, the Support Vector Machine technique proved to be the best classification method with the higher accuracy in classification. With these tested results, it could be possible to implement an alarmistic system, to warn the driver about his psychological and physical states, increasing the safety in the roads.O interesse em monitorizar os condutores dos veículos durante a sua condução tem vindo a aumentar ao longo dos anos, com o objectivo de tornar as estradas mais seguras para condutores e peões. Com este pensamento, surgiu a ideia de desenvolver um sistema capaz de monitorizar a fadiga e a sonolência do condutor e, se necessário, alertá-lo sobre o seu estado físico e psicológico. O ADAS, conhecido como sendo um sistema de assistência avançada para os condutores, é um sistema que monitoriza o desempenho e o comportamento do automóvel, bem como as condições físicas e psicológicas do condutor. Este sistema pode ter um comportamento passivo, alertando os condutores para situações de perigo eminente para que o condutor consiga evitar esses perigos. O LDW, ou aviso de mudança de faixa, é capaz de alertar o condutor de uma saída involuntária de faixa e o FCW, ou aviso de colisão frontal, consegue alertar o condutor de uma colisão eminente, tendo em conta o veículo frontal. Por outro lado, o ADAS consegue concretizar acções de forma assegurar a segurança dos passageiros e dos peões. O AEB, ou travagem de emergência automática, identifica uma colisão eminente e trava sem intervenção do conduto e o LKA, ou assistente de manutenção de faixa, que movimenta o veículo para que este não saia da faixa de rodagem. Esta dissertação é baseada no projecto CardioWheel, desenvolvido pela empresa CardioID, e consiste na monitorização do sinal cardíaco do condutor e na gravação dos movimentos realizados pelo volante do veículo durante a condução. O sinal cardíaco, conhecido como ECG, é extraído através de eléctrodos secos fixados numa capa em pele colocada no volante, que conseguem captar o sinal eléctrico provocado pelo batimento cardíaco enquanto o condutor estiver com as mãos no volante. O controlo dos movimentos do volante, ou SWA, é conseguido através de um acelerómetro de 3 eixos colocado no centro do volante que grava as variações da aceleração instantânea enquanto o condutor movimenta o volante. Através dessas acelerações é possível calcular-se o ângulo de rotação do volante durante todo o percurso. Os dados adquiridos de ECG e SWA geram uma enorme quantidade de informação que tem que ser codificada de forma a reduzir a largura de banda necessária à transmissão. Técnicas no domínio do tempo, como o AZTEC, TP e o CORTES, estão bem documentadas como boas técnicas para compressão de sinal ECG onde o principal objectivo é a obtenção da pulsação cardíaca. Dadas as exigências do projecto, concluiu-se que estes métodos não seriam os melhores para preservar as características principais do sinal de forma a obter-se padrões de fadiga e sonolência. Outros métodos de codificação com e sem perdas foram testados tanto para compressão de sinal ECG como para SWA e pode-se concluir que o método híbrido de Codificação Linear Preditiva com a técnica Lempel-Ziv-Welch é o método sem perdas em que se obteve maior rácio de compressão. Por outro lado, outro método hibrido utilizando escalamento de amplitude com DWT, provou ser o método com perdas com maior rácio de compressão onde o erro quadrático médio é reduzido. A transmissão da informação comprimida é assegurada através de um módulo BLE, presente no CardioWheel, no entanto, foi possível concluir que outras tecnologias como ZigBee ou ANT seriam igualmente compatíveis com o propósito do projecto. Foi desenvolvido especificamente para este projecto um perfil BLE com a capacidade de transmitir a informação do sinal ECG e do acelerómetro em tempo real. Para detectar se o condutor está a apresentar sinais de fadiga ou sonolência, foram testados vários algoritmos de aprendizagem automática que, de acordo com a informação ECG e do acelerómetro enviada pelo volante, conseguem detectar esses padrões. A escala KSS, é uma escala subjectiva que identifica o nível de sonolência de uma pessoa e que permite a classificação do nível de sonolência do condutor. Para construir um algoritmo de inteligência artificial é necessário extrair-se características dos sinais a interpretar. Essas características têm que descrever o sinal de forma precisa para que os algoritmos de aprendizagem automática consigam interpretar e classificar cada sinal da forma adequada. Características como ritmo cardíaco ou amplitude da onda R são exemplos de características utilizadas para descrever o sinal ECG. Características como tempo com o volante estático e aceleração média são exemplos de características utilizadas para descrever o sinal de SWA. Para além das características, um algoritmo de aprendizagem automática necessita de uma base de dados que consiga cobrir todas as situações possíveis para que o algoritmo, olhando para os dados inseridos, consiga detectar os padrões nas características para cada resultado final possível. Métodos de regressão foram implementados de forma e testar o seu desempenho para um problema de classificação, no entanto, não provaram ser os melhores métodos para essa abordagem. De todas as técnicas de classificação testadas, o método de SVM, ou máquina de vectores de suporte, provou ser o que obtém melhores resultados de classificação. Com os resultados obtidos será possível implementar-se um sistema de alarmística que consiga avisar o condutor sobre o seu estado físico e psicológico, aumentando assim a segurança rodoviária.N/

Secure steganography, compression and diagnoses of electrocardiograms in wireless body sensor networks

Submission of this completed form results in your thesis/project being lodged online at the RMIT Research Repository. Further information about the RMIT Research Repository is available at http://researchbank.rmit.edu.au Please complete abstract and keywords below for cataloguing and indexing your thesis/project. Abstract (Minimum 200 words, maximum 500 words) The usage of e-health applications is increasing in the modern era. Remote cardiac patients monitoring application is an important example of these e-health applications. Diagnosing cardiac disease in time is of crucial importance to save many patients lives. More than 3.5 million Australians suffer from long-term cardiac diseases. Therefore, in an ideal situation, a continuous cardiac monitoring system should be provided for this large number of patients. However, health-care providers lack the technology required to achieve this objective. Cloud services can be utilized to fill the technology gap for health-care providers. However, three main problems prevent health-care providers from using cloud services. Privacy, performance and accuracy of diagnoses. In this thesis we are addressing these three problems. To provide strong privacy protection services, two steganography techniques are proposed. Both techniques could achieve promising results in terms of security and distortion measurement. The differences between original and resultant watermarked ECG signals were less then 1%. Accordingly, the resultant ECG signal can be still used for diagnoses purposes, and only authorized persons who have the required security information, can extract the hidden secret data in the ECG signal. Consequently, to solve the performance problem of storing huge amount of data concerning ECG into the cloud, two types of compression techniques are introduced: Fractal based lossy compression technique and Gaussian based lossless compression technique. This thesis proves that, fractal models can be efficiently used in ECG lossy compression. Moreover, the proposed fractal technique is a multi-processing ready technique that is suitable to be implemented inside a cloud to make use of its multi processing capability. A high compression ratio could be achieved with low distortion effects. The Gaussian lossless compression technique is proposed to provide a high compression ratio. Moreover, because the compressed files are stored in the cloud, its services should be able to provide automatic diagnosis capability. Therefore, cloud services should be able to diagnose compressed ECG files without undergoing a decompression stage to reduce additional processing overhead. Accordingly, the proposed Gaussian compression provides the ability to diagnose the resultant compressed file. Subsequently, to make use of this homomorphic feature of the proposed Gaussian compression algorithm, in this thesis we have introduced a new diagnoses technique that can be used to detect life-threatening cardiac diseases such as Ventricular Tachycardia and Ventricular Fibrillation. The proposed technique is applied directly to the compressed ECG files without going through the decompression stage. The proposed technique could achieve high accuracy results near to 100% for detecting Ventricular Arrhythmia and 96% for detecting Left Bundle Branch Block. Finally, we believe that in this thesis, the first steps towards encouraging health-care providers to use cloud services have been taken. However, this journey is still long

The Compression of IoT Operational Data Time Series in Automatic Weather Station (AWS)

This project examines compression algorithms for time series data from Automatic Weather Station (AWS). The main goal of the project is to provide a solution using data compression algorithm to reduce the amount of data that needs to be transmitted from AWS to the server. Also to provide information about the comparison of algorithms that are suitable for application in AWS data. By reducing the size of the data, we can decrease the cost of transmission. Four compression algorithms are compared, those are Huffman Code, Arithmetic Coding, Lempel Ziv 7 (LZ77), and Lempel Ziv 4 (LZ4). At the end, the simulation and analysis have been carried out based on the results of experiments that have been done in the laboratory

Optimal Resource Allocation Using Deep Learning-Based Adaptive Compression For Mhealth Applications

In the last few years the number of patients with chronic diseases that require constant monitoring increases rapidly; which motivates the researchers to develop scalable remote health applications. Nevertheless, transmitting big real-time data through a dynamic network limited by the bandwidth, end-to-end delay and transmission energy; will be an obstacle against having an efficient transmission of the data. The problem can be resolved by applying data reduction techniques on the vital signs at the transmitter side and reconstructing the data at the receiver side (i.e. the m-Health center). However, a new problem will be introduced which is the ability to receive the vital signs at the server side with an acceptable distortion rate (i.e. deformation of vital signs because of inefficient data reduction). In this thesis, we integrate efficient data reduction with wireless networking to deliver an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. A Deep Learning (DL) approach was used to implement an adaptive compression technique to compress and reconstruct the vital signs in general and specifically the Electroencephalogram Signal (EEG) with the minimum distortion. Then, a resource allocation framework was introduced to minimize the transmission energy along with the distortion of the reconstructed signa

A 65nm CMOS lossless bio-signal compression circuit with 250 femtoJoule performance per bit.

A 65nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel `xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the `simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250fJ/bit at continuous conversion data-rates of 5MSa/sec. This is achieved with a diminutive circuit area of 155 um2. Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization