3,456 research outputs found
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
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
Nuevo dispositivo para análisis de voz de pacientes con enfermedad de Parkinson en tiempo real
RESUMEN: La enfermedad de Parkinson (EP) es un desorden neurodegenerativo que afecta la coordinación de músculos y extremidades, incluyendo aquellos responsables de la producción del habla, generando alteraciones en la inteligibilidad de la señal de voz. Está demostrado que el ejercicio terapéutico constante puede mejorar las habilidades de comunicación de los pacientes; sin embargo, el diagnóstico acerca del avance en el proceso de recuperación es realizado de forma subjetiva por los fonoaudiólogos o neurólogos. Debido a esto se requiere el desarrollo de herramientas flexibles que valoren y guíen la terapia fonoaudiológica de los pacientes. En este artículo se presenta el diseño e implementación de un sistema embebido para el análisis en tiempo real de la voz de pacientes con EP. Para esto se desarrollan tres plataformas; primero, se construye una interfaz gráfica en Matlab; luego, se crea un primer prototipo basado en un DSP TMS320C6713 de Texas Instruments. La aplicación final es desarrollada sobre un mini-ordenador que cuenta con un códec de audio, capacidad de almacenamiento, y una unidad de procesamiento. El sistema además se complementa con un monitor LCD para desplegar información en tiempo real, y un teclado para la interacción con el usuario. En todas las plataformas se evalúan diferentes medidas usadas comúnmente en la valoración de la voz de pacientes con EP, incluyendo características acústicas y de dinámica no lineal. En concordancia con otros trabajos del estado del arte donde se analiza la voz de personas con EP, la plataforma diseñada muestra un incremento en la variación del pitch en la voz de los pacientes, además de un decremento en el valor del área del espacio vocálico. Este resultado indica que la herramienta diseñada puede ser útil para hacer la evaluación y seguimiento de la terapia fonoaudiológica de pacientes con EP.ABSTRACT: Parkinson’s disease (PD) is a neurodegenerative disorder that affects the coordination of muscles and limbs, including those responsible of the speech production. The lack of control of the limbs and muscles involved in the speech production process can generate intelligibility problems and this situation has a negative impact in the social interaction of the patients. It is already demonstrated that constant speech therapy can improve the communication abilities of the patients; however, the measurement of the recovery progress is done subjectively by speech therapists and neurologists. Due to this, it is required the development of flexible tools able to asses and guide the speech therapy of the patients. In this paper the design and deployment of a new device for the real time assessment of speech signals of people with PD is presented. The processes of design and deployment include the development on three platforms: first, a graphic user interface is developed on Matlab, second the first prototype is implemented on a digital signal processor (DSP) and third, the final device is developed on a mini-computer. The device is equipped with an audio codec, storage capacity and the processing unit. Besides, the system is complemented with a monitor to display the processed information on real time and with a keyboard enabling the interaction of the end-user with the device. Different acoustics and nonlinear dynamics measures which have been used in the state of the art for the assessment of speech of people with PD are implemented on the three mentioned platforms. In accordance with the state of the art, the designed platforms show an increment in the variation of the
fundamental period of speech (commonly called pitch) of people with PD. Additionally, the decrease of the vocal space area is validated for the case of patients with PD. These results indicate that the designed device is useful to perform the assessment and monitoring of the speech therapy of people with PD
Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics
Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis
Bio-Radar Applications for Remote Vital Signs Monitoring
Nowadays, most vital signs monitoring techniques used in a medical context and/or daily
life routines require direct contact with skin, which can become uncomfortable or even
impractical to be used regularly. Radar technology has been appointed as one of the most
promising contactless tools to overcome these hurdles. However, there is a lack of studies
that cover a comprehensive assessment of this technology when applied in real-world
environments. This dissertation aims to study radar technology for remote vital signs
monitoring, more specifically, in respiratory and heartbeat sensing.
Two off-the-shelf radars, based on impulse radio ultra-wideband and frequency modu lated continuous wave technology, were customized to be used in a small proof of concept
experiment with 10 healthy participants. Each subject was monitored with both radars
at three different distances for two distinct conditions: breathing and voluntary apnea.
Signals processing algorithms were developed to detect and estimate respiratory and
heartbeat parameters, assessed using qualitative and quantitative methods.
Concerning respiration, a minimum error of 1.6% was found when radar respiratory
peaks signals were directly compared with their reference, whereas a minimum mean
absolute error of 0.3 RPM was obtained for the respiration rate. Concerning heartbeats,
their expression in radar signals was not as clear as the respiration ones, however a
minimum mean absolute error of 1.8 BPM for heartbeat was achieved after applying a
novel selective algorithm developed to validate if heart rate value was estimated with
reliability.
The results proved the potential for radars to be used in respiratory and heartbeat
contactless sensing, showing that the employed methods can be already used in some mo tionless situations. Notwithstanding, further work is required to improve the developed
algorithms in order to obtain more robust and accurate systems.Atualmente, a maioria das técnicas usadas para a monitorização de sinais vitais em
contexto médicos e/ou diário requer contacto direto com a pele, o que poderá tornar-se
incómodo ou até mesmo inviável em certas situações. A tecnologia radar tem vindo a ser
apontada como uma das mais promissoras ferramentas para medição de sinais vitais à
distância e sem contacto. Todavia, são necessários mais estudos que permitam avaliar esta
tecnologia quando aplicada a situações mais reais. Esta dissertação tem como objetivo o
estudo da tecnologia radar aplicada no contexto de medição remota de sinais vitais, mais
concretamente, na medição de atividade respiratória e cardíaca.
Dois aparelhos radar, baseados em tecnologia banda ultra larga por rádio de impulso
e em tecnologia de onda continua modulada por frequência, foram configurados e usados
numa prova de conceito com 10 participantes. Cada sujeito foi monitorizado com cada
um dos radar em duas situações distintas: respirando e em apneia voluntária. Algorit mos de processamento de sinal foram desenvolvidos para detetar e estimar parâmetros
respiratórios e cardíacos, avaliados através de métodos qualitativos e quantitativos.
Em relação à respiração, o menor erro obtido foi de 1,6% quando os sinais de radar
respiratórios foram comparados diretamente com os sinais de referência, enquanto que,
um erro médio absoluto mínimo de 0,3 RPM foi obtido para a estimação da frequência
respiratória via radar. A expressão cardíaca nos sinais radar não se revelou tão evidente
como a respiratória, no entanto, um erro médio absoluto mínimo de 1,8 BPM foi obtido
para a estimação da frequência cardíaca após a aplicação de um novo algoritmo seletivo,
desenvolvido para validar a confiança dos valores obtidos.
Os resultados obtidos provaram o potencial do uso de radares na medição de atividade
respiratória e cardíaca sem contacto, sendo esta tecnologia viável de ser implementada em
situações onde não existe muito movimento. Não obstante, os algoritmos desenvolvidos
devem ser aperfeiçoados no futuro de forma a obter sistemas mais robustos e precisos
Multifractal Characterization of Protein Contact Networks
The multifractal detrended fluctuation analysis of time series is able to
reveal the presence of long-range correlations and, at the same time, to
characterize the self-similarity of the series. The rich information derivable
from the characteristic exponents and the multifractal spectrum can be further
analyzed to discover important insights about the underlying dynamical process.
In this paper, we employ multifractal analysis techniques in the study of
protein contact networks. To this end, initially a network is mapped to three
different time series, each of which is generated by a stationary unbiased
random walk. To capture the peculiarities of the networks at different levels,
we accordingly consider three observables at each vertex: the degree, the
clustering coefficient, and the closeness centrality. To compare the results
with suitable references, we consider also instances of three well-known
network models and two typical time series with pure monofractal and
multifractal properties. The first result of notable interest is that time
series associated to proteins contact networks exhibit long-range correlations
(strong persistence), which are consistent with signals in-between the typical
monofractal and multifractal behavior. Successively, a suitable embedding of
the multifractal spectra allows to focus on ensemble properties, which in turn
gives us the possibility to make further observations regarding the considered
networks. In particular, we highlight the different role that small and large
fluctuations of the considered observables play in the characterization of the
network topology
Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM
We introduce a new rotationally invariant viewing angle classification method
for identifying, among a large number of Cryo-EM projection images, similar
views without prior knowledge of the molecule. Our rotationally invariant
features are based on the bispectrum. Each image is denoised and compressed
using steerable principal component analysis (PCA) such that rotating an image
is equivalent to phase shifting the expansion coefficients. Thus we are able to
extend the theory of bispectrum of 1D periodic signals to 2D images. The
randomized PCA algorithm is then used to efficiently reduce the dimensionality
of the bispectrum coefficients, enabling fast computation of the similarity
between any pair of images. The nearest neighbors provide an initial
classification of similar viewing angles. In this way, rotational alignment is
only performed for images with their nearest neighbors. The initial nearest
neighbor classification and alignment are further improved by a new
classification method called vector diffusion maps. Our pipeline for viewing
angle classification and alignment is experimentally shown to be faster and
more accurate than reference-free alignment with rotationally invariant K-means
clustering, MSA/MRA 2D classification, and their modern approximations
Electrochemical Noise Measurement Technique in Corrosion Research
Electrochemical noise measurement is one of the novel techniques currently being used in corrosion monitoring. Two major methods of analysis in use are the Fast Fourier Transform (FFT) and the Maximum Entropy Method (MEM). This paper reviews the techniques fundamental background – types of noise, physical data; description, classification and characteristics; mathematical background of random data and spectral analysis. Recent progress made in its application to corrosion monitoring and other electrochemical reaction phenomena are also examined
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