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About

About

Raquel Sebastião is postdoctoral researcher at Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, and at LIAAD - Artificial Intelligence and Decision Support Laboratory (R&D laboratory belonging to INESC TEC). She is PhD in Applied Mathematics, with background in Applied Mathematics and Computational Methods, from University of Porto.

She participated in several national and international R&D projects on multi-modality medical image registration, incremental and adaptive learning systems, modelling and control for personalized drug administration, image self-similarity and decision support in first responders scenarios.

Her research activity has been in facial recognition, registration of multimodal images, data streams, synopsis structures, change detection approaches, decision support systems and biomedical applications. Currently, she is focused on data mining and information extraction from physiological signals. She has teaching experience at polytechnic institutes and programming skills.

Interest
Topics
Details

Details

  • Name

    Raquel Sebastião
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st January 2010
Publications

2018

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

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

Publication
Evolving Systems

Abstract

2017

Fading histograms in detecting distribution and concept changes

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

Publication
I. J. Data Science and Analytics

Abstract

2017

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

Authors
Sebastião, R; Fernandes, JM;

Publication
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

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

Publication
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

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

Publication
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.