1,560 research outputs found

    Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

    Full text link
    In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

    Analyse des signaux AM-FM basée sur une version B-splines de l'EMD-ESA

    Get PDF
    In this paper a signal analysis framework for estimating time-varying amplitude and frequency functions of multicomponent amplitude and frequency modulated (AM–FM) signals is introduced. This framework is based on local and non-linear approaches, namely Energy Separation Algorithm (ESA) and Empirical Mode Decomposition (EMD). Conjunction of Discrete ESA (DESA) and EMD is called EMD–DESA. A new modified version of EMD where smoothing instead of an interpolation to construct the upper and lower envelopes of the signal is introduced. Since extracted IMFs are represented in terms of B-spline (BS) expansions, a closed formula of ESA robust against noise is used. Instantaneous Frequency (IF) and Instantaneous Amplitude (IA) estimates of a multi- component AM–FM signal, corrupted with additive white Gaussian noise of varying SNRs, are analyzed and results compared to ESA, DESA and Hilbert transform-based algorithms. SNR and MSE are used as figures of merit. Regularized BS version of EMD– ESA performs reasonably better in separating IA and IF components compared to the other methods from low to high SNR. Overall, obtained results illustrate the effective- ness of the proposed approach in terms of accuracy and robustness against noise to track IF and IA features of a multicomponent AM–FM signal

    Some Advances in Nonlinear Speech Modeling Using Modulations, Fractals, and Chaos

    Get PDF
    In this paper we briefly summarize our on-going work on modeling nonlinear structures in speech signals, caused by modulation and turbulence phenomena, using the theories of modulation, fractals, and chaos as well as suitable nonlinear signal analysis methods. Further, we focus on two advances: i) AM-FM modeling of fricative sounds with random modulation signals of the 1/f-noise type and ii) improved methods for speech analysis and prediction on reconstructed multidimensional attractors. 1

    Data-driven multivariate and multiscale methods for brain computer interface

    Get PDF
    This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

    Automatic Speech Recognition Using LP-DCTC/DCS Analysis Followed by Morphological Filtering

    Get PDF
    Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals. This work develops filtering structures based on a mathematical morphology that utilizes the bias while emphasizing spectral peaks. The combination of peak emphasis via LP analysis with morphological filtering results in more noise robust speech recognition rates. To help understand the behavior of these pre-processing techniques the deterministic and statistical properties of the morphological filters are compared to the properties of feature extraction techniques that do not employ such algorithms. The robust behavior of these algorithms for automatic speech recognition in the presence of rapidly fluctuating speech signals with additive and convolutional noise is illustrated. Examples of these nonlinear feature extraction techniques are given using the Aurora 2.0 and Aurora 3.0 databases. Features are computed using LP analysis alone to emphasize peaks, morphological filtering alone, or a combination of the two approaches. Although absolute best results are normally obtained using a combination of the two methods, morphological filtering alone is nearly as effective and much more computationally efficient
    • …
    corecore