704 research outputs found
Flexible Time Series Matching for Clinical and Behavioral Data
Time Series data became broadly applied by the research community in the last decades after
a massive explosion of its availability. Nonetheless, this rise required an improvement
in the existing analysis techniques which, in the medical domain, would help specialists
to evaluate their patients condition. One of the key tasks in time series analysis is pattern
recognition (segmentation and classification). Traditional methods typically perform subsequence
matching, making use of a pattern template and a similarity metric to search
for similar sequences throughout time series. However, real-world data is noisy and variable
(morphological distortions), making a template-based exact matching an elementary
approach. Intending to increase flexibility and generalize the pattern searching tasks
across domains, this dissertation proposes two Deep Learning-based frameworks to solve
pattern segmentation and anomaly detection problems.
Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is
proposed, learning to distinguish, point-by-point, desired sub-patterns from background
content within a time series. The proposed framework was validated in two use-cases:
electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed
two conventional matching techniques, being capable of notably detecting the
targeted cycles even in noise-corrupted or extremely distorted signals, without using any
reference template nor hand-coded similarity scores.
Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction
ability of Variational Autoencoders and a local similarity score to identify
non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH
Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results
indicated competitiveness relative to recent techniques, achieving detection AUC scores
of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de sĂ©ries temporais tornaram-se largamente aplicados pela comunidade cientĂfica
nas Ășltimas decadas apĂłs um aumento massivo da sua disponibilidade. Contudo, este
aumento exigiu uma melhoria das atuais tĂ©cnicas de anĂĄlise que, no domĂnio clĂnico, auxiliaria
os especialistas na avaliação da condição dos seus pacientes. Um dos principais
tipos de anålise em séries temporais é o reconhecimento de padrÔes (segmentação e classificação).
MĂ©todos tradicionais assentam, tipicamente, em tĂ©cnicas de correspondĂȘncia em
subsequĂȘncias, fazendo uso de um padrĂŁo de referĂȘncia e uma mĂ©trica de similaridade
para procurar por subsequĂȘncias similares ao longo de sĂ©ries temporais. Todavia, dados
do mundo real sĂŁo ruidosos e variĂĄveis (morfologicamente), tornando uma correspondĂȘncia
exata baseada num padrĂŁo de referĂȘncia uma abordagem rudimentar. Pretendendo
aumentar a flexibilidade da anålise de séries temporais e generalizar tarefas de procura
de padrĂ”es entre domĂnios, esta dissertação propĂ”e duas abordagens baseadas em Deep
Learning para solucionar problemas de segmentação de padrÔes e deteção de anomalias.
Acerca da segmentação de padrÔes, a rede neuronal de Convolução/Deconvolução
proposta aprende a distinguir, ponto a ponto, sub-padrĂ”es pretendidos de conteĂșdo de
fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais
eletrocardiogrĂĄficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou
duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente,
mesmo em sinais corrompidos por ruĂdo ou extremamente distorcidos, sem o uso de
nenhum padrĂŁo de referĂȘncia nem mĂ©tricas de similaridade codificadas manualmente.
A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a
capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade
local para identificar anomalias desconhecidas. A proposta foi validada na identificação
de arritmias cardĂacas em duas bases de dados pĂșblicas de ECG (MIT-BIH Arrhythmia e
ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando
métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)
Scoring and assessment in medical VR training simulators with dynamic time series classification
This is the author accepted manuscript. the final version is available from Elsevier via the DOI in this recordThis research proposes and evaluates scoring and assessment methods for Virtual Reality (VR) training simulators. VR simulators capture detailed n-dimensional human motion data which is useful for performance analysis. Custom made medical haptic VR training simulators were developed
and used to record data from 271 trainees of multiple clinical experience levels. DTW Multivariate Prototyping (DTW-MP) is proposed. VR data was
classified as Novice, Intermediate or Expert. Accuracy of algorithms applied
for time-series classification were: dynamic time warping 1-nearest neighbor
(DTW-1NN) 60%, nearest centroid SoftDTW classification 77.5%, Deep Learning: ResNet 85%, FCN 75%, CNN 72.5% and MCDCNN 28.5%. Expert VR
data recordings can be used for guidance of novices. Assessment feedback can
help trainees to improve skills and consistency. Motion analysis can identify
different techniques used by individuals. Mistakes can be detected dynamically
in real-time, raising alarms to prevent injuries.Royal Academy of Engineering (RAEng)University of ExeterUniversity of Technology SydneyBournemouth Universit
Patterns in Motion - From the Detection of Primitives to Steering Animations
In recent decades, the world of technology has developed rapidly. Illustrative of this trend is the growing number of affrdable methods for recording new and bigger data sets. The resulting masses of multivariate and high-dimensional data represent a new challenge for research and industry. This thesis is dedicated to the development of novel methods for processing multivariate time series data, thus meeting this Data Science related challenge. This is done by introducing a range of different methods designed to deal with time series data. The variety of methods re ects the different requirements and the typical stage of data processing ranging from pre-processing to post- processing and data recycling. Many of the techniques introduced work in a general setting. However, various types of motion recordings of human and animal subjects were chosen as representatives of multi-variate time series. The different data modalities include Motion Capture data, accelerations, gyroscopes, electromyography, depth data (Kinect) and animated 3D-meshes. It is the goal of this thesis to provide a deeper understanding of working with multi-variate time series by taking the example of multi-variate motion data. However, in order to maintain an overview of the matter, the thesis follows a basic general pipeline. This pipeline was developed as a guideline for time series processing and is the first contribution of this work. Each part of the thesis represents one important stage of this pipeline which can be summarized under the topics segmentation, analysis and synthesis. Specific examples of different data modalities, processing requirements and methods to meet those are discussed in the chapters of the respective parts. One important contribution of this thesis is a novel method for temporal segmentation of motion data. It is based on the idea of self-similarities within motion data and is capable of unsupervised segmentation of range of motion data into distinct activities and motion primitives. The examples concerned with the analysis of multi-variate time series re ect the role of data analysis in different inter-disciplinary contexts and also the variety of requirements that comes with collaboration with other sciences. These requirements are directly connected to current challenges in data science. Finally, the problem of synthesis of multi-variate time series is discussed using a graph-based example and examples related to rigging or steering of meshes. Synthesis is an important stage in data processing because it creates new data from existing ones in a controlled way. This makes exploiting existing data sets and and access of more condensed data possible, thus providing feasible alternatives to otherwise time-consuming manual processing.Muster in Bewegung - Von der Erkennung von Primitiven zur Steuerung von Animationen In den letzten Jahrzehnten hat sich die Welt der Technologie rapide entwickelt. Beispielhaft fĂŒr diese Entwicklung ist die wachsende Zahl erschwinglicher Methoden zum Aufzeichnen neuer und immer gröĂerer Datenmengen. Die sich daraus ergebenden Massen multivariater und hochdimensionaler Daten stellen Forschung wie Industrie vor neuartige Probleme. Diese Arbeit ist der Entwicklung neuer Verfahren zur Verarbeitung multivariater Zeitreihen gewidmet und stellt sich damit einer groĂen Herausforderung, welche unmittelbar mit dem neuen Feld der sogenannten Data Science verbunden ist. In ihr werden ein Reihe von verschiedenen Verfahren zur Verarbeitung multivariater Zeitserien eingefĂŒhrt. Die verschiedenen Verfahren gehen jeweils auf unterschiedliche Anforderungen und typische Stadien der Datenverarbeitung ein und reichen von Vorverarbeitung bis zur Nachverarbeitung und darĂŒber hinaus zur Wiederverwertung. Viele der vorgestellten Techniken eignen sich zur Verarbeitung allgemeiner multivariater Zeitreihen. Allerdings wurden hier eine Anzahl verschiedenartiger Aufnahmen von menschlichen und tierischen Subjekte ausgewĂ€hlt, welche als Vertreter fĂŒr allgemeine multivariate Zeitreihen gelten können. Zu den unterschiedlichen ModalitĂ€ten der Aufnahmen gehören Motion Capture Daten, Beschleunigungen, Gyroskopdaten, Elektromyographie, Tiefenbilder ( Kinect ) und animierte 3D -Meshes. Es ist das Ziel dieser Arbeit, am Beispiel der multivariaten Bewegungsdaten ein tieferes Verstndnis fĂŒr den Umgang mit multivariaten Zeitreihen zu vermitteln. Um jedoch einen Ăberblick ber die Materie zu wahren, folgt sie jedoch einer grundlegenden und allgemeinen Pipeline. Diese Pipeline wurde als Leitfaden fĂŒr die Verarbeitung von Zeitreihen entwickelt und ist der erste Beitrag dieser Arbeit. Jeder weitere Teil der Arbeit behandelt eine von drei gröĂeren Stationen in der Pipeline, welche sich unter unter die Themen Segmentierung, Analyse und Synthese eingliedern lassen. Beispiele verschiedener DatenmodalitĂ€ten und Anforderungen an ihre Verarbeitung erlĂ€utern die jeweiligen Verfahren. Ein wichtiger Beitrag dieser Arbeit ist ein neuartiges Verfahren zur zeitlichen Segmentierung von Bewegungsdaten. Dieses basiert auf der Idee der SelbstĂ€hnlichkeit von Bewegungsdaten und ist in der Lage, verschiedenste Bewegungsdaten voll-automatisch in unterschiedliche AktivitĂ€ten und Bewegungs-Primitive zu zerlegen. Die Beispiele fr die Analyse multivariater Zeitreihen spiegeln die Rolle der Datenanalyse in verschiedenen interdisziplinĂ€ren ZusammenhĂ€nge besonders wider und illustrieren auch die Vielfalt der Anforderungen, die sich in interdisziplinĂ€ren Kontexten auftun. SchlieĂlich wird das Problem der Synthese multivariater Zeitreihen unter Verwendung eines graph-basierten und eines Steering Beispiels diskutiert. Synthese ist insofern ein wichtiger Schritt in der Datenverarbeitung, da sie es erlaubt, auf kontrollierte Art neue Daten aus vorhandenen zu erzeugen. Dies macht die Nutzung bestehender DatensĂ€tze und den Zugang zu dichteren Datenmodellen möglich, wodurch Alternativen zur ansonsten zeitaufwendigen manuellen Verarbeitung aufgezeigt werden
Subject-independent modeling of sEMG signals for the motion of a single robot joint through GMM Modelization
This thesis evaluates the use of a probabilistic model, the Gaussian Mixture Model (GMM), trained through Electromyography (EMG) signals to estimate the bending angle of a single human joint. The GMM is created from the EMG signals collected by different people and the goal is to create a general model based on the data of different subjects. The model is then tested on new, unseen data. The goodness of the estimated data is evaluated by means of Normalized Mean Square Errorope
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