170 research outputs found

    From Compression of Wearable-based Data to Effortless Indoor Positioning

    Get PDF
    In recent years, wearable devices have become ever-present in modern society. They are typically defined as small, battery-restricted devices, worn on, in, or in very close proximity to a human body. Their performance is defined by their functionalities as much as by their comfortability and convenience. As such, they need to be compact yet powerful, thus making energy efficiency an extremely important and relevant aspect of the system. The market of wearable devices is nowadays dominated by smartwatches and fitness bands, which are capable of gathering numerous sensor-based data such as temperature, pressure, heart rate, or blood oxygen level, which have to be processed in real-time, stored, or wirelessly transferred while consuming as little energy as possible to ensure long battery life. Implementing compression schemes directly at the wearable device is one of the relevant methods to reduce the volume of data and to minimize the number of required operations while processing them, as raw measurements include plenty of redundancies that can be removed without damaging the useful information itself. This thesis presents a number of contributions in the field of compression of wearable-based data, mainly in areas of lossy compression techniques designated for the time series sensor-based data and positioning. In the scope of this work, two novel time-series compression techniques are proposed, namely Direct Lightweight Temporal Compression (DLTC) and Altered Symbolic Aggregate Approximation (ASAX), which are specifically designed to address relevant challenges of modern wearable systems. As many of the modern wearables also possess localization capabilities critical for navigation, tracking, and monitoring applications, reducing the computational and storage demands for indoor positioning applications is the second addressed challenge. Performing the positioning task quickly and efficiently on all connected devices, including wearables, becomes crucial in industrial applications, eHealth, or security. As the localization technique of choice in Global Navigation Satellite System (GNSS) signal-obscured scenarios, positioning via fingerprinting proves a reliable and efficient solution, while arising new challenges to be solved. Improving the efficiency of the fingerprinting-based system by applying lossy compressions onto the training radio map is realized by proposing, implementing, and evaluating various novel dimensionality-reduction techniques. This thesis proposes Element-Wise cOmpression using K-means (EWOK), a bitlevel compression based on element-wise k-means clustering, radio Map compression Employing Signal Statistics (MESS), a sample-wise compression that extracts signal statistics based on their locations, as well as evaluates feature-wise methods Principal Component Analysis (PCA) and Auto-Encoder (AE) that transform fingerprints into low-dimensional representation. The evaluation in the thesis shows the effectiveness of each compression scheme on 26 different datasets and provides the results achieved by combining the individual schemes together, accomplishing multi-dimensional radio map compression that sustains high positioning accuracy of the dataset, despite manyfold size reduction. The processing requirements of the positioning system are further addressed by proposing a cascade of models that reduces the required search space of the algorithm. By combining numerous Machine Learning (ML) architectures, it is capable of further reducing the positioning time (and thus, positioning effort), while improving the positioning performance. The thesis further includes the introduction of an indoor positioning dataset collected by the author, denoted TUJI 1, a novel performance metric to evaluate the latency caused by the lossy compression, and several crucial adjustments to the distance metric calculations, generalizing their applicability. The thesis provides novel insights into the compression of sensor-based, timeseries data and into reducing the computational effort of the fingerprinting positioning schemes while introducing a relevant number of novel and efficient solutions beyond the State-of-the-Art.Cotutelle -yhteisväitöskirj

    Machine Learning Methods with Noisy, Incomplete or Small Datasets

    Get PDF
    In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

    Principled methods for mixtures processing

    Get PDF
    This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the short­term research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and α­stable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences

    Serum potassium concentration monitoring by ECG time warping analysis on the T wave

    Get PDF
    This doctoral thesis was developed within the joint Ph.D. program in biomedical engineering at Universitat Politècnica de Catalunya (Barcelona, Spain) and University of Zaragoza (Zaragoza, Spain) in the framework of Doctorats Industrials program co-financed by Laboratorios Rubió S.A. (Castellbisbal, Spain) and Agència de Gestió d’Ajuts Universitaris i de Recerca, Generalitat de Catalunya (Spain). This thesis was performed in partnership with the Nephrology ward from Hospital Clínico Universitario Lozano Blesa (Zaragoza, Spain) and in collaboration with Dr J. Ramírez from the William Harvey Research Institute, Queen Mary University of London (London, UK).End-stage renal disease (ESRD) patients demonstrate an increased incidence of sudden cardiac death (SCD) with declining kidney functioning as a consequence of blood potassium ([K+]) homeostasis impairment, which is restored by hemodialysis (HD) therapy. The clinically established method for the diagnosis of [K+] imbalance is blood tests, an invasive and costly procedure that limits continuous monitoring of ESRD patients. A non-invasive ambulatory index, able to quantify changes in [K+] level is an open issue. In this context, the electrocardiogram (ECG) and in particular, the T wave (TW) morphology, has been shown to be strongly correlated with [K+] imbalance. Therefore, the aim of this dissertation is to investigate and to propose TW-derived markers able to monitor changes in [K+] levels in ESRD patients undergoing HD. For that purpose, the time warping analysis, a technique that allows the comparison and quantification of differences between two different TW shapes, was investigated. The application of TW time warping based markers in monitoring [K+ ] variations (Δ [K+]) and the derivation of a heart-rate corrected marker is proposed and compared with respect to two well-established Δ [K+]-related TW-based indexes. All the markers are evaluated in a single lead approach and after having emphasised the TW energy content through spatial transformation by Principal Component Analysis (PCA). Results demonstrate that the proposed biomarkers outperform the already proposed indexes, also proving that the use of PCA transformed lead generates markers with a higher correlation with Δ [K+] than the single lead approach. The possibility to improve markers robustness in the case of low signal-to-noise ratio ECGs, by spatially transforming the signal maximising the beat-to-beat TW periodicity criteria through the so-called Periodic Component Analysis (pCA), is then explored. pCA-based markers show superior performance during and after the HD than those obtained by PCA suggesting improved stability for continuous Δ [K+] tracking. The thesis studies also the application of regressions models to quantify Δ [K+] from pCA-based time warping markers. The accuracy of the regression models is evaluated by correlation and estimation error between the actual and the corresponding model-estimated Δ [K+] values, and the smallest estimation error is found for quadratic regression models. Being the time warping derived markers sensitive to TW boundary delineation errors, which may endanger their prognostic power, the advantages of using a weighting stage is investigated for their robust computation. The performance of two weighting functions (WF)s is tested and compared with respect to the control no weighting case, in simulated scenarios and in real scenarios (i.e. for [K+] monitoring and SCD risk stratification). No improvements in [K+] monitoring are found, probably due to the considerable marked [K+]-induced TW morphological changes. On the contrary, both simulation tests and SCD risk stratification analysis show that the proposed WFs can enhance the robustness of TW time warping analysis against TW delineation errors. In conclusion, this Doctoral Thesis confirms the hypothesis that enhanced perforce in Δ [K+] tracking and quantification can be achieved by analysing the overall TW morphology by time warping analysis. The simplicity of the technology, together with its low cost and ease of acquisition, should provide a new opportunity for TW analysis to reach standard clinical practice. Moreover, the use of WFs to minimise the undesired effects of TW delineation errors on the computation of time warping markers revealed a noticeable improvement of the SCD risk stratification power of time warping derived indexes.Los pacientes con enfermedad renal en etapa terminal (ESRD) demuestran una mayor incidencia de muerte cardíaca súbita (SCD) tras el deterioro del funcionamiento renal como consecuencia del desequilibrio del potasio ([K+]) en sangre. Este último se restablece mediante la terapia de hemodiálisis (HD). El desequilibrio de [K+] se diagnostica a través del análisis de sangre, un procedimiento invasivo y costoso que limita la monitorización de los pacientes con ESRD. Se necesita un índice ambulatorio no invasivo, capaz de cuantificar los cambios en el nivel de [K+] (Δ [K+]). En este contexto, se ha demostrado que el electrocardiograma (ECG) y en particular la onda T (TW), están correlacionados con Δ [K+]. El objetivo de esta tesis es evaluar marcadores derivados de la TW capaces de monitorizar ¿[K+] en pacientes con ESRD sometidos a HD. Para ello, se aplicó el análisis time warping, una técnica que permite la comparación de dos formas diferentes de TW. En primer lugar, se evalúa la aplicación de marcadores basados en el time warping para el seguimiento de Δ [K+] así como la derivación de un marcador corregido por la frecuencia cardíaca, comparando los marcadores con respecto a dos índices basados en TW bien establecidos y relacionados con Δ [K+]. Todos los marcadores se evalúan en las derivaciones independientes y después de haber enfatizado el contenido de energía de TW a través del Análisis de Componentes Principales (PCA). Los resultados demuestran mejores prestaciones de los marcadores time warping respecto a los ya propuestos y que el uso de PCA genera marcadores con una correlación más alta con Δ [K+] respecto a las derivaciones independientes. A continuación, se explora la posibilidad de mejorar la robustez de los marcadores en el caso de ECG con una relación señal/ruido baja, maximizando la periodicidad de TW latido a latido mediante el Análisisde Componentes Periódicos (pCA). Los marcadores basados en pCA muestran un rendimiento superior durante y después de la HD que los obtenidos por PCA, lo que sugiere una estabilidad mejorada para el seguimiento continuo de Δ [K+]. Luego, se evalúan modelos de regresión para cuantificar [K+] a partir de marcadores basados en pCA. La precisión de los modelos de regresión se evalúa mediante el error de estimación entre valores reales de Δ [K+] y los correspondientes estimados por el modelo. Con el error de estimación más pequeño, el modelo cuadrático es el más adecuado para la cuantificación de [K+].Siendo el análisis time warping sensible a los errores de delineación de los límites de TW, lo que supone un riesgo para su poder pronóstico, se investigan las ventajas de usar una etapa de ponderación para el cálculo de marcadores time warping. El rendimiento de dos funciones de ponderación (WF) se prueba y se compara con respecto al caso de control sin ponderación, en escenarios simulados y en escenarios reales (para el seguimiento de [K+] y la estratificación del riesgo de SCD). No se encontraron mejoras en la monitorización de [K+] debido a los considerables cambios morfológicos de TW inducidos por Δ [K+]. Por otro lado, tanto las pruebas de simulación como el análisis de estratificación de riesgo de SCD muestran que los WF propuestos pueden mejorar la robustez del análisis time warping de TW contra los errores dedelineación de TW. En conclusión, esta tesis doctoral confirma la hipótesis de que se puede lograr un mejor seguimiento y cuantificación de Δ [K+] mediante el análisis de la morfología de TW mediante el análisis time warping. La simplicidad de la tecnología, junto con su bajo costo y facilidad de adquisición del ECG, debería brindar una nueva oportunidad para que el análisis de TW en la práctica clínica rutinaria. Además, el uso de WF para minimizar los efectos no deseados de errores de delineación de TW en el cálculo de los marcadores time warping reveló una mejora del poder de estratificación del riesgoPostprint (published version

    Serum potassium concentration monitoring by ECG time warping analysis on the T wave

    Get PDF
    This doctoral thesis was developed within the joint Ph.D. program in biomedical engineering at Universitat Politècnica de Catalunya (Barcelona, Spain) and University of Zaragoza (Zaragoza, Spain) in the framework of Doctorats Industrials program co-financed by Laboratorios Rubió S.A. (Castellbisbal, Spain) and Agència de Gestió d’Ajuts Universitaris i de Recerca, Generalitat de Catalunya (Spain). This thesis was performed in partnership with the Nephrology ward from Hospital Clínico Universitario Lozano Blesa (Zaragoza, Spain) and in collaboration with Dr J. Ramírez from the William Harvey Research Institute, Queen Mary University of London (London, UK).End-stage renal disease (ESRD) patients demonstrate an increased incidence of sudden cardiac death (SCD) with declining kidney functioning as a consequence of blood potassium ([K+]) homeostasis impairment, which is restored by hemodialysis (HD) therapy. The clinically established method for the diagnosis of [K+] imbalance is blood tests, an invasive and costly procedure that limits continuous monitoring of ESRD patients. A non-invasive ambulatory index, able to quantify changes in [K+] level is an open issue. In this context, the electrocardiogram (ECG) and in particular, the T wave (TW) morphology, has been shown to be strongly correlated with [K+] imbalance. Therefore, the aim of this dissertation is to investigate and to propose TW-derived markers able to monitor changes in [K+] levels in ESRD patients undergoing HD. For that purpose, the time warping analysis, a technique that allows the comparison and quantification of differences between two different TW shapes, was investigated. The application of TW time warping based markers in monitoring [K+ ] variations (Δ [K+]) and the derivation of a heart-rate corrected marker is proposed and compared with respect to two well-established Δ [K+]-related TW-based indexes. All the markers are evaluated in a single lead approach and after having emphasised the TW energy content through spatial transformation by Principal Component Analysis (PCA). Results demonstrate that the proposed biomarkers outperform the already proposed indexes, also proving that the use of PCA transformed lead generates markers with a higher correlation with Δ [K+] than the single lead approach. The possibility to improve markers robustness in the case of low signal-to-noise ratio ECGs, by spatially transforming the signal maximising the beat-to-beat TW periodicity criteria through the so-called Periodic Component Analysis (pCA), is then explored. pCA-based markers show superior performance during and after the HD than those obtained by PCA suggesting improved stability for continuous Δ [K+] tracking. The thesis studies also the application of regressions models to quantify Δ [K+] from pCA-based time warping markers. The accuracy of the regression models is evaluated by correlation and estimation error between the actual and the corresponding model-estimated Δ [K+] values, and the smallest estimation error is found for quadratic regression models. Being the time warping derived markers sensitive to TW boundary delineation errors, which may endanger their prognostic power, the advantages of using a weighting stage is investigated for their robust computation. The performance of two weighting functions (WF)s is tested and compared with respect to the control no weighting case, in simulated scenarios and in real scenarios (i.e. for [K+] monitoring and SCD risk stratification). No improvements in [K+] monitoring are found, probably due to the considerable marked [K+]-induced TW morphological changes. On the contrary, both simulation tests and SCD risk stratification analysis show that the proposed WFs can enhance the robustness of TW time warping analysis against TW delineation errors. In conclusion, this Doctoral Thesis confirms the hypothesis that enhanced perforce in Δ [K+] tracking and quantification can be achieved by analysing the overall TW morphology by time warping analysis. The simplicity of the technology, together with its low cost and ease of acquisition, should provide a new opportunity for TW analysis to reach standard clinical practice. Moreover, the use of WFs to minimise the undesired effects of TW delineation errors on the computation of time warping markers revealed a noticeable improvement of the SCD risk stratification power of time warping derived indexes.Los pacientes con enfermedad renal en etapa terminal (ESRD) demuestran una mayor incidencia de muerte cardíaca súbita (SCD) tras el deterioro del funcionamiento renal como consecuencia del desequilibrio del potasio ([K+]) en sangre. Este último se restablece mediante la terapia de hemodiálisis (HD). El desequilibrio de [K+] se diagnostica a través del análisis de sangre, un procedimiento invasivo y costoso que limita la monitorización de los pacientes con ESRD. Se necesita un índice ambulatorio no invasivo, capaz de cuantificar los cambios en el nivel de [K+] (Δ [K+]). En este contexto, se ha demostrado que el electrocardiograma (ECG) y en particular la onda T (TW), están correlacionados con Δ [K+]. El objetivo de esta tesis es evaluar marcadores derivados de la TW capaces de monitorizar ¿[K+] en pacientes con ESRD sometidos a HD. Para ello, se aplicó el análisis time warping, una técnica que permite la comparación de dos formas diferentes de TW. En primer lugar, se evalúa la aplicación de marcadores basados en el time warping para el seguimiento de Δ [K+] así como la derivación de un marcador corregido por la frecuencia cardíaca, comparando los marcadores con respecto a dos índices basados en TW bien establecidos y relacionados con Δ [K+]. Todos los marcadores se evalúan en las derivaciones independientes y después de haber enfatizado el contenido de energía de TW a través del Análisis de Componentes Principales (PCA). Los resultados demuestran mejores prestaciones de los marcadores time warping respecto a los ya propuestos y que el uso de PCA genera marcadores con una correlación más alta con Δ [K+] respecto a las derivaciones independientes. A continuación, se explora la posibilidad de mejorar la robustez de los marcadores en el caso de ECG con una relación señal/ruido baja, maximizando la periodicidad de TW latido a latido mediante el Análisisde Componentes Periódicos (pCA). Los marcadores basados en pCA muestran un rendimiento superior durante y después de la HD que los obtenidos por PCA, lo que sugiere una estabilidad mejorada para el seguimiento continuo de Δ [K+]. Luego, se evalúan modelos de regresión para cuantificar [K+] a partir de marcadores basados en pCA. La precisión de los modelos de regresión se evalúa mediante el error de estimación entre valores reales de Δ [K+] y los correspondientes estimados por el modelo. Con el error de estimación más pequeño, el modelo cuadrático es el más adecuado para la cuantificación de [K+].Siendo el análisis time warping sensible a los errores de delineación de los límites de TW, lo que supone un riesgo para su poder pronóstico, se investigan las ventajas de usar una etapa de ponderación para el cálculo de marcadores time warping. El rendimiento de dos funciones de ponderación (WF) se prueba y se compara con respecto al caso de control sin ponderación, en escenarios simulados y en escenarios reales (para el seguimiento de [K+] y la estratificación del riesgo de SCD). No se encontraron mejoras en la monitorización de [K+] debido a los considerables cambios morfológicos de TW inducidos por Δ [K+]. Por otro lado, tanto las pruebas de simulación como el análisis de estratificación de riesgo de SCD muestran que los WF propuestos pueden mejorar la robustez del análisis time warping de TW contra los errores dedelineación de TW. En conclusión, esta tesis doctoral confirma la hipótesis de que se puede lograr un mejor seguimiento y cuantificación de Δ [K+] mediante el análisis de la morfología de TW mediante el análisis time warping. La simplicidad de la tecnología, junto con su bajo costo y facilidad de adquisición del ECG, debería brindar una nueva oportunidad para que el análisis de TW en la práctica clínica rutinaria. Además, el uso de WF para minimizar los efectos no deseados de errores de delineación de TW en el cálculo de los marcadores time warping reveló una mejora del poder de estratificación del riesgoEnginyeria biomèdic

    Sensors for Vital Signs Monitoring

    Get PDF
    Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

    Applications of Medical Physics

    Get PDF
    Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

    Towards Real-Time Non-Stationary Sinusoidal Modelling of Kick and Bass Sounds for Audio Analysis and Modification

    Get PDF
    Sinusoidal Modelling is a powerful and flexible parametric method for analysing and processing audio signals. These signals have an underlying structure that modern spectral models aim to exploit by separating the signal into sinusoidal, transient, and noise components. Each of these can then be modelled in a manner most appropriate to that component's inherent structure. The accuracy of the estimated parameters is directly related to the quality of the model's representation of the signal, and the assumptions made about its underlying structure. For sinusoidal models, these assumptions generally affect the non-stationary estimates related to amplitude and frequency modulations, and the type of amplitude change curve. This is especially true when using a single analysis frame in a non-overlapping framework, where biased estimates can result in discontinuities at frame boundaries. It is therefore desirable for such a model to distinguish between the shape of different amplitude changes and adapt the estimation of this accordingly. Intra-frame amplitude change can be interpreted as a change in the windowing function applied to a stationary sinusoid, which can be estimated from the derivative of the phase with respect to frequency at magnitude peaks in the DFT spectrum. A method for measuring monotonic linear amplitude change from single-frame estimates using the first-order derivative of the phase with respect to frequency (approximated by the first-order difference) is presented, along with a method of distinguishing between linear and exponential amplitude change. An adaption of the popular matching pursuit algorithm for refining model parameters in a segmented framework has been investigated using a dictionary comprised of sinusoids with parameters varying slightly from model estimates, based on Modelled Pursuit (MoP). Modelling of the residual signal using a segmented undecimated Wavelet Transform (segUWT) is presented. A generalisation for both the forward and inverse transforms, for delay compensations and overlap extensions for different lengths of Wavelets and the number of decomposition levels in an Overlap Save (OLS) implementation for dealing with convolution block-based artefacts is presented. This shift invariant implementation of the DWT is a popular tool for de-noising and shows promising results for the separation of transients from noise

    Models and analysis of vocal emissions for biomedical applications

    Get PDF
    This book of Proceedings collects the papers presented at the 4th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2005, held 29-31 October 2005, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies
    corecore