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Author (down) Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez edit   pdf
doi  openurl
  Title A reduced feature set for driver head pose estimation Type Journal Article
  Year 2016 Publication Applied Soft Computing Abbreviated Journal ASOC  
  Volume 45 Issue Pages 98-107  
  Keywords Head pose estimation; driving performance evaluation; subspace based methods; linear regression  
  Abstract Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.  
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  Notes ADAS; 600.085; 600.076; Approved no  
  Call Number Admin @ si @ DHL2016 Serial 2760  
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Author (down) Jaume Garcia; Debora Gil; Luis Badiella; Aura Hernandez-Sabate; Francesc Carreras; Sandra Pujades; Enric Marti edit   pdf
doi  openurl
  Title A Normalized Framework for the Design of Feature Spaces Assessing the Left Ventricular Function Type Journal Article
  Year 2010 Publication IEEE Transactions on Medical Imaging Abbreviated Journal TMI  
  Volume 29 Issue 3 Pages 733-745  
  Keywords  
  Abstract A through description of the left ventricle functionality requires combining complementary regional scores. A main limitation is the lack of multiparametric normality models oriented to the assessment of regional wall motion abnormalities (RWMA). This paper covers two main topics involved in RWMA assessment. We propose a general framework allowing the fusion and comparison across subjects of different regional scores. Our framework is used to explore which combination of regional scores (including 2-D motion and strains) is better suited for RWMA detection. Our statistical analysis indicates that for a proper (within interobserver variability) identification of RWMA, models should consider motion and extreme strains.  
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  Series Volume Series Issue Edition  
  ISSN 0278-0062 ISBN Medium  
  Area Expedition Conference  
  Notes IAM Approved no  
  Call Number IAM @ iam @ GGH2010b Serial 1507  
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Author (down) Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda edit  doi
openurl 
  Title PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities Type Journal
  Year 2015 Publication Journal of Technology and Science Education Abbreviated Journal JOTSE  
  Volume 5 Issue 2 Pages 87-96  
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  Notes IAM; ADAS; 600.076; 600.075 Approved no  
  Call Number Admin @ si @ MRG2015 Serial 2608  
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Author (down) Debora Gil; Aura Hernandez-Sabate; Oriol Rodriguez; Josepa Mauri; Petia Radeva edit   pdf
doi  openurl
  Title Statistical Strategy for Anisotropic Adventitia Modelling in IVUS Type Journal Article
  Year 2006 Publication IEEE Transactions on Medical Imaging Abbreviated Journal  
  Volume 25 Issue 6 Pages 768-778  
  Keywords Corners; T-junctions; Wavelets  
  Abstract Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and mediaadventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders. Index Terms–-Anisotropic processing, intravascular ultrasound (IVUS), vessel border segmentation, vessel structure classification.  
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  Notes IAM;MILAB Approved no  
  Call Number IAM @ iam @ GHR2006 Serial 1525  
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Author (down) Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta edit   pdf
doi  openurl
  Title Structure-preserving smoothing of biomedical images Type Journal Article
  Year 2011 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 44 Issue 9 Pages 1842-1851  
  Keywords Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography  
  Abstract Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.  
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  Series Volume Series Issue Edition  
  ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ GHB2011 Serial 1526  
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Author (down) Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera edit  doi
openurl 
  Title E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights Type Journal Article
  Year 2022 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume 10 Issue Pages 7489-7503  
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  Abstract More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.		  
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  Notes IAM; 600.139; 600.118; 600.145 Approved no  
  Call Number Admin @ si @ GHE2022 Serial 3721  
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Author (down) Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin edit   pdf
url  openurl
  Title Mathematics learning opportunities when playing a Tower Defense Game Type Journal
  Year 2015 Publication International Journal of Serious Games Abbreviated Journal IJSG  
  Volume 2 Issue 4 Pages 57-71  
  Keywords Tower Defense game; learning opportunities; mathematics; problem solving; game design  
  Abstract A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.  
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  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ HJG2015 Serial 2730  
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Author (down) Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez edit  doi
openurl 
  Title Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem Type Journal
  Year 2020 Publication Mathematics Abbreviated Journal MATH  
  Volume 20 Issue 8(9) Pages 1595  
  Keywords STEM education; Project-based learning; Coding; software tool  
  Abstract In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view.		  
  Address September 2020  
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  Area Expedition Conference  
  Notes IAM; ISE Approved no  
  Call Number Admin @ si @ Serial 3722  
Permanent link to this record
 
 
Author (down) Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil edit  doi
openurl 
  Title Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals Type Journal Article
  Year 2022 Publication Applied Sciences Abbreviated Journal APPLSCI  
  Volume 12 Issue 5 Pages 2298  
  Keywords Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion  
  Abstract The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.  
  Address February 2022  
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  Notes IAM; ADAS; 600.139; 600.145; 600.118 Approved no  
  Call Number Admin @ si @ HYF2022 Serial 3720  
Permanent link to this record
 
 
Author (down) Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil edit  url
openurl 
  Title EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment Type Journal Article
  Year 2024 Publication Sensors Abbreviated Journal SENS  
  Volume 24 Issue 4 Pages 1174  
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  Abstract High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.  
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  Notes IAM Approved no  
  Call Number Admin @ si @ HYF2024 Serial 4019  
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