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Sobre

Sobre

Raquel Sebastião é investigadora pós-doutorada no IEETA – Instituto de Engenharia Eletrónica e Telemática de Aveiro, Universidade de Aveiro, e no LIAAD – Laboratório de Inteligência Artificial e Apoio à Decisão (laboratório de I&D pertencente ao INESC TEC). É doutorada em Matemática Aplicada, com formação inicial em Matemática Aplicada e Métodos Computacionais, pela Universidade do Porto.

Participou em vários projetos de I&D nacionais e internacionais em diferentes áreas, tais como: registo multi-modal de imagens médicas, sistemas de aprendizagem incrementais e adaptativos, modelação e controlo para a administração personalizada de fármacos, similaridade de imagens e apoio à decisão em profissionais de primeira resposta.

A sua de investigação tem sido em reconhecimento facial, registo de imagens multi-modais, fluxos contínuos de dados, estruturas de sumarização, métodos de deteção de mudanças, sistemas de apoio à decisão e aplicações biomédicas. Atualmente trabalha em data mining e extração de informação de sinais fisiológicos. Tem ainda experiência em lecionação em escolas politécnicas e capacidade de programação.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Raquel Sebastião
  • Cluster

    Informática
  • Cargo

    Investigador Colaborador Externo
  • Desde

    01 janeiro 2010
Publicações

2019

Detecting changes in the heart rate of firefighters to prevent smoke inhalation and health effects

Autores
Sebastiao, R; Sorte, S; Valente, J; Miranda, AI; Fernandes, JM;

Publicação
Evolving Systems

Abstract

2017

Fading histograms in detecting distribution and concept changes

Autores
Sebastião, R; Gama, J; Mendonça, T;

Publicação
I. J. Data Science and Analytics

Abstract

2017

Supporting the page-hinkley test with empirical mode decomposition for change detection

Autores
Sebastião, R; Fernandes, JM;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
In the dynamic scenarios faced nowadays, when handling non stationary data streams it is of utmost importance to perform change detection tests. In this work, we propose the Intrinsic Page Hinkley Test (iPHT), which enhances the Page Hinkley Test (PHT) eliminating the user-defined parameter (the allowed magnitude of change of the data that are not considered real distribution change of the data stream) by using the second order intrinsic mode function (IMF) which is a data dependent value reflecting the intrinsic data variation. In such way, the PHT change detection method is expected to be more robust and require less tunes. Furthermore, we extend the proposed iPHT to a blockwise approach. Computing the IMF over sliding windows, which is shown to be more responsive to changes and suitable for online settings. The iPHT is evaluated using artificial and real data, outperforming the PHT. © Springer International Publishing AG 2017.

2016

Inhalation during fire experiments: An approach derived through ECG

Autores
Sebastião, R; Sorte, S; Valente, J; Miranda, AI; Fernandes, JM;

Publicação
UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Abstract
During forest fire fights, firefighters are exposed to several pollutants at different concentrations, which can induce critical health problems. This study main goal is to estimate firefighters' pollutants inhalation when in operational scenarios by combining environmental and physiological information. Both exposures to CO (carbon monoxide) and physiological data, such as ECG (electrocardiogram), HR (Heart Rate) and body temperature, were monitored during firefighters' activities in experimental forest fire. From the QRS complex of ECG the ECG-derived respiration (EDR) was estimated and convoluted with pollutants concentration to estimate individual smoke inhalation. The analysis of smoke inhalations allowed to detect extensive exposures and to identify critical situations namely risk of faint due to smoke intoxication. Our results support the usefulness of continuous monitoring of both physiological and environmental information to prevent and detect hazardous situations while firefighters are in operational scenario like forest fires. The results encourage the development of a decision support system to be applied in real-Time during firefighting scenarios. © 2016 ACM.

2016

Inhalation during fire experiments: An approach derived through ECG

Autores
Sebastião, R; Sorte, S; Valente, J; Miranda, AI; Fernandes, JM;

Publicação
UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Abstract
During forest fire fights, firefighters are exposed to several pollutants at different concentrations, which can induce critical health problems. This study main goal is to estimate firefighters' pollutants inhalation when in operational scenarios by combining environmental and physiological information. Both exposures to CO (carbon monoxide) and physiological data, such as ECG (electrocardiogram), HR (Heart Rate) and body temperature, were monitored during firefighters' activities in experimental forest fire. From the QRS complex of ECG the ECG-derived respiration (EDR) was estimated and convoluted with pollutants concentration to estimate individual smoke inhalation. The analysis of smoke inhalations allowed to detect extensive exposures and to identify critical situations namely risk of faint due to smoke intoxication. Our results support the usefulness of continuous monitoring of both physiological and environmental information to prevent and detect hazardous situations while firefighters are in operational scenario like forest fires. The results encourage the development of a decision support system to be applied in real-Time during firefighting scenarios. © 2016 ACM.