Conferences related to Health Monitoring System

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2014 IEEE Sensors Applications Symposium (SAS)

SAS provides a forum for sensor users and developers to meet and exchange information about novel sensors and emergent sensor applications. The main purpose of SAS is to collaborate and network with scientists,engineers, researchers, developers, and end-users through formal technical presentations, workshops, and informal interactions.

  • 2013 IEEE Sensors Applications Symposium (SAS)

    SAS 2013 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS 2013 include: Biosensors /Arrays, MEMS and Nanosensors, Sensor Networking, Smart Sensors and Standards, Virtual Sensors, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestructive Evaluation and Remote Sensing, Homeland security, and Commercial Development.

  • 2012 IEEE Sensors Applications Symposium (SAS)

    SAS 2012 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS 2012 include: Biosensors /Arrays, MEMS and Nanosensors, Sensor Networking, Smart Sensors and Standards, Virtual Sensors, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestructive Evaluation and Remote Sensing, Homeland security, and Commercial Development.

  • 2011 IEEE Sensors Applications Symposium (SAS)

    SAS -2010 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS -2010 include: Biosensors /Arrays, MEMS and Nanosensors , Wireless and Networked Sensors, Smart Sensors and Standards, Virtual Sensors, Radiation detection and standards, Integrated System Health Management (ISHM), Multisensor Data Fusion.

  • 2010 IEEE Sensors Applications Symposium (SAS)

    SAS-2010 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS-2010 include: Biosensors /Arrays, MEMS and Nanosensors , Wireless and Networked Sensors, Smart Sensors and Standards, Virtual Sensors, Radiation detection and standards, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestructive Evaluation and Remote

  • 2009 IEEE Sensors Applications Symposium (SAS)

    SAS-2009 provides a forum for sensor users and developers to exchange information about novel and emergent applications in smart sensors, homeland security, biology, system health management, and related areas. Suggested topics for SAS-2009 include: Biosensors /Arrays, MEMS and Nanosensors , Wireless and Networked Sensors, Smart Sensors and Standards, Virtual Sensors, Radiation detection and standards, X-ray detectors and imaging, Integrated System Health Management (ISHM), Multisensor Data Fusion, Nondestr


2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT)

Presentations from LI Section Chapters and Long Island-based industry covering systems, applications, technology advances on Long Island


SICE 2012 - 51st Annual Conference of the Society of Instrument and Control Engineers of Japan

This conference covers a wide range of fields from measurement and control to system analysis and design, from theory to application and from software to hardware.


2011 IEEE International Workshop on Open-source Software for Scientific Computation (OSSC)

University of Science and Technology Beijing


2010 3rd International Conference on Computational Intelligence and Industrial Application (PACIIA)

PACIIA 2010 serves as a forum for researchers, industry professionals, and academics interested in the latest development and design of Computational Intelligence and Industrial Applications.

  • 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA 2009)

    T-1 Fuzzy Logic, Neural Networks T-2 GA and Evolutionary Computation T-3 Adaptation and Learning Systems T-4 Distributed Intelligent Systems T-5 Network Systems T-6 Intelligent Robotics T-7 Control Systems and Applications T-8 Signal & Image Processing T-9 Intelligent Human Interface T-10 Wireless and Mobile Networks in Industry T-11 Information Security in Industry T-12 Other Scientific Applications and Industrial Applications.

  • 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA 2008)

    T-1 Fuzzy Logic, Neural Networks T-2 GA and Evolutionary Computation T-3 Adaptation and Learning Systems T-4 Distributed Intelligent Systems T-5 Network Systems T-6 Intelligent Robotics T-7 Control Systems and Applications T-8 Signal & Image Processing T-9 Intelligent Human Interface T-10 Wireless and Mobile Networks in Industry T-11 Information Security in Industry T-12 Other Scientific Applications and Industrial Applications


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Periodicals related to Health Monitoring System

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Information Technology in Biomedicine, IEEE Transactions on

Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.


Reliability, IEEE Transactions on

Principles and practices of reliability, maintainability, and product liability pertaining to electrical and electronic equipment.


Solid-State Circuits, IEEE Journal of

The IEEE Journal of Solid-State Circuits publishes papers each month in the broad area of solid-state circuits with particular emphasis on transistor-level design of integrated circuits. It also provides coverage of topics such as device modeling, technology, systems design, layout, and testing that relate directly to IC design. Integrated circuits and VLSI are of principal interest; material related to discrete ...



Most published Xplore authors for Health Monitoring System

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Xplore Articles related to Health Monitoring System

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Rule-Based Motor Fault Detection

L. Eren; O. Baskirt; M. J. Devaney; L. Eren; O. Baskirt; M. J. Devaney 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings, 2005

Motor current signature analysis (MCSA) provides a non-intrusive way of assessing health of a motor. In this paper, we propose a rule based motor health monitoring system that is capable of detecting faults at varying operating conditions. Motor current is non-stationary due varying load conditions. Wavelet packet decomposition is utilized in this study for processing of non-stationary motor current


Spiral bevel gear damage detection using decision fusion analysis

P. J. Dempsey; R. F. Handschuh; A. A. Afjeh Information Fusion, 2002. Proceedings of the Fifth International Conference on, 2002

A diagnostic tool for detecting damage to spiral bevel gears was developed. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated ...


Detection of building structure damage with support vector machine

Salvador Villegas; Xiaoou Li; Wen Yu Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on, 2015

An important objective of health monitoring systems (HMS) for tall building is to diagnose the state of the building and to detect its possible damage. To solve these problems, data mining approaches are becoming meaningful along with the advance of Big Data techniques, among which support vector machine (SVM) is one of the most powerful classifiers because of its good ...


Ultra Low Power Granular Decision Making Using Cross Correlation: Optimizing Bit Resolution for Template Matching

Hassan Ghasemzadeh; Roozbeh Jafari 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium, 2011

Advances in technology have led to development of wearable sensing, computing and communication devices that can be woven into the physical environment of our daily lives, enabling a large variety of new applications in several domains including wellness and health care. Despite their tremendous potential to impact our lives, wearable health monitoring systems face a number of hurdles to become ...


Design and implementation of wireless bridge health monitoring system

Fang Zhang; Rui Wang; Shilin Gao; Shuai Yu; Jiayun Hu; Yanliang Jin Smart and Sustainable City (ICSSC 2011), IET International Conference on, 2011

On account of the existing problems, which are complex, costly and difficult to maintain, in the bridge monitoring systems, we present a system based on the Zigbee technology. This article introduces the method of establishing the system from the perspective of practical implementation of hardware and software. We design the hardware using the TI's CC2530 chip which is a Single- ...


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Educational Resources on Health Monitoring System

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eLearning

Rule-Based Motor Fault Detection

L. Eren; O. Baskirt; M. J. Devaney; L. Eren; O. Baskirt; M. J. Devaney 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings, 2005

Motor current signature analysis (MCSA) provides a non-intrusive way of assessing health of a motor. In this paper, we propose a rule based motor health monitoring system that is capable of detecting faults at varying operating conditions. Motor current is non-stationary due varying load conditions. Wavelet packet decomposition is utilized in this study for processing of non-stationary motor current


Spiral bevel gear damage detection using decision fusion analysis

P. J. Dempsey; R. F. Handschuh; A. A. Afjeh Information Fusion, 2002. Proceedings of the Fifth International Conference on, 2002

A diagnostic tool for detecting damage to spiral bevel gears was developed. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated ...


Detection of building structure damage with support vector machine

Salvador Villegas; Xiaoou Li; Wen Yu Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on, 2015

An important objective of health monitoring systems (HMS) for tall building is to diagnose the state of the building and to detect its possible damage. To solve these problems, data mining approaches are becoming meaningful along with the advance of Big Data techniques, among which support vector machine (SVM) is one of the most powerful classifiers because of its good ...


Ultra Low Power Granular Decision Making Using Cross Correlation: Optimizing Bit Resolution for Template Matching

Hassan Ghasemzadeh; Roozbeh Jafari 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium, 2011

Advances in technology have led to development of wearable sensing, computing and communication devices that can be woven into the physical environment of our daily lives, enabling a large variety of new applications in several domains including wellness and health care. Despite their tremendous potential to impact our lives, wearable health monitoring systems face a number of hurdles to become ...


Design and implementation of wireless bridge health monitoring system

Fang Zhang; Rui Wang; Shilin Gao; Shuai Yu; Jiayun Hu; Yanliang Jin Smart and Sustainable City (ICSSC 2011), IET International Conference on, 2011

On account of the existing problems, which are complex, costly and difficult to maintain, in the bridge monitoring systems, we present a system based on the Zigbee technology. This article introduces the method of establishing the system from the perspective of practical implementation of hardware and software. We design the hardware using the TI's CC2530 chip which is a Single- ...


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IEEE.tv Videos

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IEEE-USA E-Books

  • Index

    Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain- computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world. Guido Dornhege is a Postdoctoral Researcher in the Intelligent Data Analysis Group at the Fraunhofer Institute for Computer Architecture and Software Technology in Berlin. Josï¿¿ï¿¿ del R. Millï¿¿ï¿¿n is a Senior Researcher at the IDIAP Research Institute in Martigny, Switzerland, and Adjunct Professor at the Swiss Federal Institute of Technology in Lausanne. Thilo Hinterberger is with the Institute of Medical Psychology at the University of Tï¿¿ï¿¿bingen and is a Senior Researcher at the University of Northampton. Dennis J. McFarland is a Research Scientist with the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health. Klaus-Robert Mï¿ ¿ï¿¿ller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin.

  • Challenges In The Use of Wireless Sensor Networks for Monitoring The Health of Civil Structures

    This chapter provides an overview of the challenges faced in the design of new techniques for enabling new decentralized solutions of large-scale wireless sensor networks (WSNs) in the structural health monitoring (SHM) domain. It introduces the definition of SHM along with the concept of WSNs. The chapter discusses the concepts of SHM and WSNs apart from one another. The chapter explains existent solutions employing WSNs in the context of SHM. It focuses on SHM techniques based on the use of accelerometers; but in further investigations, this classification can be expanded to works that use other kinds of sensing devices, for example, strain gauges, following the same logic of higher degrees of decentralization and in-network processing. The concept of generations of sensor networks for SHM was used for such classification. Each generation is presented by describing respective examples of works found in the current literature.

  • Power System Disturbance Analysis Function

    This chapter contains sections titled: Analysis Function of Power System Disturbances Objective of DFR Disturbance Analysis Determination of Power System Equipment Health Through System Disturbance Analysis Description of DFR Equipment Information Required for the Analysis of System Disturbances Signals to be Monitored by a Fault Recorder DFR Trigger Settings of Monitored Voltages and Currents DFR and Numerical Relay Sampling Rate and Frequency Response Oscillography Fault Records Generated by Numerical Relaying Integration and Coordination of Data Collected from Intelligent Electronic Devices DFR Software Analysis Packages Verification of DFR Accuracy in Monitoring Substation Ground Currents Using DFR Records to Validate Power System Short-Circuit Study Models COMTRADE Standard References

  • Health-Integrated System Paradigm: Diabetes Management

    Diabetes mellitus is a common disease, affecting millions of people in Europe and throughout the world. People suffering from chronic diseases like diabetes are ideal candidates for telemonitoring. In the last decade, technological progress in telemedicine has had a positive impact on health care management. It is now possible to provide telemetric services to a large number of patients using relatively simple telemetric applications. This chapter presents METABO system, focusing particularly on the context of its implementation, the user requirements, and problems faced and solved during the development process. The METABO system has been contrasted to the state of the art in telemedical diabetes management in order to underline its innovativeness. The main innovation is the ability to analyze and interpret the context-sensitive fusion of information from both body and environmental monitoring noninvasive devices and depict a complete framework of patient's status, enabling behavior prediction and consequent feedback generation.

  • References

    Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain- computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world. Guido Dornhege is a Postdoctoral Researcher in the Intelligent Data Analysis Group at the Fraunhofer Institute for Computer Architecture and Software Technology in Berlin. Josï¿¿ï¿¿ del R. Millï¿¿ï¿¿n is a Senior Researcher at the IDIAP Research Institute in Martigny, Switzerland, and Adjunct Professor at the Swiss Federal Institute of Technology in Lausanne. Thilo Hinterberger is with the Institute of Medical Psychology at the University of Tï¿¿ï¿¿bingen and is a Senior Researcher at the University of Northampton. Dennis J. McFarland is a Research Scientist with the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health. Klaus-Robert Mï¿ ¿ï¿¿ller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin.

  • No title

    Breath sounds have long been important indicators of respiratory health and disease. Acoustical monitoring of respiratory sounds has been used by researchers for various diagnostic purposes. A few decades ago, physicians relied on their hearing to detect any symptomatic signs in respiratory sounds of their patients. However, with the aid of computer technology and digital signal processing techniques in recent years, breath sound analysis has drawn much attention because of its diagnostic capabilities. Computerized respiratory sound analysis can now quantify changes in lung sounds; make permanent records of the measurements made and produce graphical representations that help with the diagnosis and treatment of patients suffering from lung diseases. Digital signal processing techniques have been widely used to derive characteristics features of the lung sounds for both diagnostic and assessment of treatment purposes. Although the analytical techniques of signal processing are largely ndependent of the application, interpretation of their results on biological data, i.e. respiratory sounds, requires substantial understanding of the involved physiological system. This lecture series begins with an overview of the anatomy and physiology related to human respiratory system, and proceeds to advanced research in respiratory sound analysis and modeling, and their application as diagnostic aids. Although some of the used signal processing techniques have been explained briefly, the intention of this book is not to describe the analytical methods of signal processing but the application of them and how the results can be interpreted. The book is written for engineers with university level knowledge of mathematics and digital signal processing.

  • No title

    A medical device is an apparatus that uses engineering and scientific principles to interface to physiology and diagnose or treat a disease. In this Lecture, we specifically consider thosemedical devices that are computer based, and are therefore referred to as medical instruments. Further, the medical instruments we discuss are those that incorporate system theory into their designs. We divide these types of instruments into those that provide continuous observation and those that provide a single snapshot of health information. These instruments are termed patient monitoring devices and diagnostic devices, respectively.Within this Lecture, we highlight some of the common system theory techniques that are part of the toolkit of medical device engineers in industry. These techniques include the pseudorandom binary sequence, adaptive filtering, wavelet transforms, the autoregressive moving average model with exogenous input, artificial neural networks, fuzzy models, and fuzzy control. ecause the clinical usage requirements for patient monitoring and diagnostic devices are so high, system theory is the preferred substitute for heuristic, empirical processing during noise artifact minimization and classification. Table of Contents: Preface / Medical Devices / System Theory / Patient Monitoring Devices / Diagnostic Devices / Conclusion / Author Biography

  • Contributors

    Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain- computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world. Guido Dornhege is a Postdoctoral Researcher in the Intelligent Data Analysis Group at the Fraunhofer Institute for Computer Architecture and Software Technology in Berlin. Josï¿¿ï¿¿ del R. Millï¿¿ï¿¿n is a Senior Researcher at the IDIAP Research Institute in Martigny, Switzerland, and Adjunct Professor at the Swiss Federal Institute of Technology in Lausanne. Thilo Hinterberger is with the Institute of Medical Psychology at the University of Tï¿¿ï¿¿bingen and is a Senior Researcher at the University of Northampton. Dennis J. McFarland is a Research Scientist with the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health. Klaus-Robert Mï¿ ¿ï¿¿ller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin.

  • No title

    This book presents physics-based electro-thermal models of bipolar power semiconductor devices including their packages, and describes their implementation in MATLAB and Simulink. It is a continuation of our first book Modeling of Bipolar Power Semiconductor Devices. The device electrical models are developed by subdividing the devices into different regions and the operations in each region, along with the interactions at the interfaces, are analyzed using the basic semiconductor physics equations that govern device behavior. The Fourier series solution is used to solve the ambipolar diffusion equation in the lightly doped drift region of the devices. In addition to the external electrical characteristics, internal physical and electrical information, such as junction voltages and carrier distribution in different regions of the device, can be obtained using the models. The instantaneous dissipated power, calculated using the electrical device models, serves as input to the thermal m del (RC network with constant and nonconstant thermal resistance and thermal heat capacity, or Fourier thermal model) of the entire module or package, which computes the junction temperature of the device. Once an updated junction temperature is calculated, the temperature-dependent semiconductor material parameters are re-calculated and used with the device electrical model in the next time-step of the simulation. The physics-based electro-thermal models can be used for optimizing device and package design and also for validating extracted parameters of the devices. The thermal model can be used alone for monitoring the junction temperature of a power semiconductor device, and the resulting simulation results used as an indicator of the health and reliability of the semiconductor power device.

  • Accelerometer-Based Body Sensor Network (BSN) for Medical Diagnosis Assessment and Training

    Body sensor network (BSN) can provide real-time remote monitoring of the health situation of a particular person. In operation, wearable, miniaturized, and low-power consumption sensors are attached on or implanted in human body for collecting biological signals. In this chapter, the use of accelerometers for BSN for monitoring the body motions is discussed. Recent advances in the computation of motion identification are described including tilting angle, muscle strength, and gait performance and specific computing algorithms, different interpretations of the signal from accelerometers can be formulated. Moreover, several medical diagnosis assessment and training applications are introduced to demonstrate the capability of an accelerometer-based BSN. Furthermore, the concept of using biped humanoid robots to develop the BSN simulation system is discussed. Finally, the BSN simulation system could generate helpful BSN information for evaluating the algorithms' performance in laboratories before the BSN systems are deployed to the human's body.



Standards related to Health Monitoring System

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No standards are currently tagged "Health Monitoring System"