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Analysis of Time Series for the Intelligent Support to Early Mobilization with Machine Learning - TFM:356

Student:Martin Colville, Alexander David
Language:Anglès
Title in original language:Analysis of Time Series for the Intelligent Support to Early Mobilization with Machine Learning
Title in different languages:Analysis of Time Series for the Intelligent Support to Early Mobilization with Machine Learning
Keywords:time series, machine learning, early mobilization
Subject:Enginyeria informàtica
Abstract:The purpose of this study is the analysis of machine learning techniques based on big data analysis and artificial neural networks to support early mobilization techniques on patients in an Intensive Care Unit. More specifically, the goal is to recognize the movement of patients to monitor their evolution overtime, dealing with time series data obtained from accelerometers. This document outlines an implementation where multiple algorithms (Support Vector Machines, Gaussian Naïve Bayes, Decision Trees and Multilayer Perceptron) are used in conjunction with time series to train models study their accuracy. Furthermore, a validation dataset is created with off-the-shelf devices to obtain new motion predictions. The project is aimed to establish two main facts: the effectivity of machine learning techniques when dealing with time series and their forecasting and the possibility of the integration of these for widespread applications.
Project director:Solanas, Agustí
Department:Enginyeria Informàtica i Matemàtiques
Education area(s):Enginyeria de la Seguretat Informàtica i Intel·ligència Artificial
Entity:Universitat Rovira i Virgili (URV)
TFM credits:9
Work's public defense date:2018-09-17
Academic year:2017-2018
Confidenciality:No
Subject areas:Computer engineering
Creation date in repository:2019-02-11
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