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University of Las Palmas de Gran Canaria, Spain

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54 Papers

Analyzing Cognitive Patterns in Gifted Children Using MRI and Morphometric Similarity Networks

Topics: Medical Image Acquisition, Processing, Analysis, and Detection; Neural Systems

Authors: Shuning Han ^{1

;

2} , Feng Duan ³ , Gemma Vilaseca ^{4

;

5} , Núria Vilaró ⁵ , Cesar Caiafa ⁶ , Zhe Sun ^{2

;

7} and Jordi Solé-Casals ^{1

;

8}

Affiliations: ¹ Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Catalonia, Spain , ² Image Processing Research Group, RIKEN Center for Advanced Photonics, RIKEN, Wako-Shi, Saitama, Japan , ³ Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, Nankai University, Tianjin, China , ⁴ Psychological Department, Oms and Prat school, Fundació Catalunya - La Pedrera, Manresa, Catalonia, Spain , ⁵ Oms Foundation, Manresa, Catalonia, Spain , ⁶ Instituto Argentino de Radioastronomía-CCT La Plata, CONICET / CIC-PBA / UNLP, Argentina , ⁷ Faculty of Health Data Science, Juntendo University, Urayasu, Chiba, Japan , ⁸ Department of Psychiatry, University of Cambridge, Cambridge, U.K.

Digital Object Identifier: 10.5220/0013169200003911

Proceedings of BIOSIGNALS 2025, ISBN: 978-989-758-731-3

Quick Abstract

Advances in non-invasive neuroimaging, such as structural magnetic resonance imaging (sMRI), have enabled the construction of structural brain networks (SBNs), allowing in vivo mapping of anatomical connections. This study investigates brain network structural differences linked to different intelligence levels in children by individual morphometric similarity networks (MSNs) derived from sMRI data. Through group- and individual-level analyses, we aim to uncover key topological features associated with cognitive performance and to identify a suitable connection density for SBN analysis. Connection density strongly affects global and nodal topological features, with a range of p = 0.05 to 0.15 recommended for stable and optimal results. Gifted individuals exhibit stronger intra-hemispheric and intra-modular connectivity, a more balanced distribution of left-to-right intra-hemispheric connections, and lower mean versatility, supporting efficient and stable cognitive processing. Moreover, anatomical modularity analyses based on von Economo indicate that higher cognitive performance is linked to enhanced connectivity in specific areas (such as secondary sensory area, motor to association area and secondary sensory to limbic area), alongside selective reduction in certain modular connections (such as motor to insular area, association to secondary sensory area and motor to secondary sensory area). Furthermore, topological features, including participation coefficient and local efficiency, are linked to cognitive performance. These findings provide valuable insights into the SBNs underlying cognitive levels in children.

Analyzing Male Depression Using Empirical Mode Decomposition

Topics: Medical Signal Acquisition, Analysis and Processing; Biometrics, Detection and Identification; Speech & Audio Processing

Authors: Xavier Corrales ^{1

;

2} , Jordi Solé-Casals ^{3

;

2} , Enrique García ⁴ and Diego Vidal ⁴

Affiliations: ¹ Researcher, Mental Health Department, Consorci Corporació Sanitària Parc Taulí, Sabadell, Barcelona, Spain , ² Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain , ³ Department of Psychiatry, University of Cambridge, Cambridge, U.K. , ⁴ Consorci Corporació Sanitària Parc Taulí, Sabadell, Barcelona, Spain

Digital Object Identifier: 10.5220/0013157600003911

Proceedings of BIOSIGNALS 2025, ISBN: 978-989-758-731-3

Quick Abstract

This study investigates the differences in male voice between healthy individuals and individuals with depression, using Empirical Mode Decomposition (EMD) analysis. Voice recordings from 25 men with depression and 76 without were analyzed. The methodology consisted of extracting 16 Intrinsic Mode Functions (IMFs) from 20-second voice segments, followed by statistical analyses including bootstrapping of means and standard deviations with False Discovery Rate (FDR) correction, comparison of probability density functions, and the application of a Gaussian kernel. The results showed significant differences between the means and standard deviations, The application of the Gaussian kernel revealed more pronounced differences in IMFs 2 to 6, providing more specific discrimination than traditional statistical methods. The study contributes to the development of non-invasive and objective diagnostic tools for depression.

An Evaluation of General-Purpose Optical Character Recognizers and Digit Detectors for Race Bib Number Recognition

Topics: Information Retrieval; Biometrics; Image and Video Analysis and Understanding

Authors: Modesto Castrillón-Santana ¹ , David Freire-Obregón ¹ , Daniel Hernández-Sosa ¹ , Oliverio Santana ¹ , Francisco Ortega-Zamorano ² , José Isern-González ¹ and Javier Lorenzo-Navarro ¹

Affiliations: ¹ SIANI, Universidad de Las Palmas de Gran Canaria, Spain , ² Universidad de Málaga, Spain

Digital Object Identifier: 10.5220/0012562400003654

Proceedings of ICPRAM 2024, ISBN: 978-989-758-684-2

Quick Abstract

Bib numbers are used in mass competitions to identify participants, especially in long-distance races where runners commonly wear tags to verify that they pass mandatory checkpoints. In this paper, we delve deeper into the use of existing computer vision techniques for recognizing the digits present in bib numbers. Our analysis of bib recognition involves evaluating OCRs (Optical Character Recognition) techniques and a YOLOv7 digit detector on two public datasets: RBNR and TGCRBNW. The results reveal that the former scenario is solvable, while the latter presents extremely in-the-wild challenges. However, the findings suggest that more than relying solely on RBN for runner identification, other appearance-based cues, e.g., clothing and accessories, may be required due to various circumstances, such as occlusion or incomplete bib recognition. In any case, all those cues do not necessarily imply that the same person is wearing the RBN across the competition track, as they are not biometric traits.

An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

Topics: Medical Image Acquisition, Processing, Analysis, and Detection; Neural Networks for Biosignal Data

Authors: Shuning Han ^{1

;

2} , Zhe Sun ^{2

;

3} , Kanhao Zhao ⁴ , Feng Duan ⁵ , Cesar F. Caiafa ⁶ , Yu Zhang ^{4

;

7} and Jordi Solé-Casals ^{1

;

8}

Affiliations: ¹ Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, 08500, Catalonia, Spain , ² Image Processing Research Group, RIKEN Center for Advanced Photonics, Riken, Wako-Shi, Saitama, Japan , ³ Faculty of Health Data Science, Juntendo University, Urayasu, Chiba, Japan , ⁴ Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, U.S.A. , ⁵ Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, Nankai University, Tianjin, China , ⁶ Instituto Argentino de Radioastronomía-CCT La Plata, CONICET/ CIC-PBA/ UNLP, V. Elisa 1894, Argentina , ⁷ Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, U.S.A. , ⁸ Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, U.K.

Digital Object Identifier: 10.5220/0012414600003657

Proceedings of BIOSIGNALS 2024, ISBN: 978-989-758-688-0

Quick Abstract

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN framework obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse applications of GCNs in brain analysis and disease detection.

Evaluating the Impact of Low-Light Image Enhancement Methods on Runner Re-Identification in the Wild

Topics: Biometrics; Image and Video Analysis and Understanding

Authors: Oliverio Santana , Javier Lorenzo-Navarro , David Freire-Obregón , Daniel Hernández-Sosa and Modesto Castrillón-Santana

Affiliation: SIANI, Universidad de Las Palmas de Gran Canaria, Spain

Digital Object Identifier: 10.5220/0011652000003411

Proceedings of ICPRAM 2023, ISBN: 978-989-758-626-2

Quick Abstract

Person re-identification (ReID) is a trending topic in computer vision. Significant developments have been achieved, but most rely on datasets with subjects captured statically within a short period of time in rather good lighting conditions. In the wild scenarios, such as long-distance races that involve widely varying lighting conditions, from full daylight to night, present a considerable challenge. This issue cannot be addressed by increasing the exposure time on the capture device, as the runners’ motion will lead to blurred images, hampering any ReID attempts. In this paper, we survey some low-light image enhancement methods. Our results show that including an image processing step in a ReID pipeline before extracting the distinctive body appearance features from the subjects can provide significant performance improvements.

Boosting Re-identification in the Ultra-running Scenario

Topics: Biometrics; Image and Video Analysis and Understanding

Authors: Miguel Ángel Medina , Javier Lorenzo-Navarro , David Freire-Obregón , Oliverio J. Santana , Daniel Hernández-Sosa and Modesto Castrillón Santana

Affiliation: SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

Digital Object Identifier: 10.5220/0010904600003122

Proceedings of ICPRAM 2022, ISBN: 978-989-758-549-4

Quick Abstract

In the context of ultra-running (longer than a marathon distance), whole-body based re-identification (ReId) state of the art approaches have reported moderated success due to the challenging unrestricted characteristics of the long-term scenario, as very different illuminations, accessories (backpacks, caps, sunglasses), and/or changes of clothes are present. In this paper, we explore the integration of two elements in the ReId process: 1) an additional biometric cue such as the face, and 2) the particular spatio-temporal context information present in these competitions. Preliminary results confirm the limited relevance of the facial cue in the (not high resolution) ReId scenario and the great benefits of the contextual information to reduce the gallery size and consequently improve the overall ReId performance.

Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App

Topics: Medical Signal Acquisition, Analysis and Processing; Monitoring and Telemetry

Authors: Chiara Capra ^{1

;

2} , Pere Marti-Puig ¹ , Daniel Vega Moreno ³ , Laia Llunas ² , Stella Nicolaou ³ , Carlos Schmidt ³ and Jordi Solé-Casals ¹

Affiliations: ¹ Data and Signal Processing Research Group, Universitat de Vic-Universitat Central de Catalunya, carrer de la Laura 13, Vic, Spain , ² beHIT, carrer de Mata 1, Barcelona, Spain , ³ Department of Psychiatry & Mental Health, Hospital d’Igualada, Fundació Sanitària Igualada, Igualada, Spain

Digital Object Identifier: 10.5220/0010978500003123

Proceedings of BIOSIGNALS 2022/BIOSTEC, ISBN: 978-989-758-552-4

Quick Abstract

Machine learning (ML) integrated with technology has been a breakthrough in mental health, bringing clinical improvements both for the patient and for the clinician. Among these, real-time patient symptoms’ tracking through ecological momentary assessment (EMA) data can be a valuable tool to forecast symptomatology at the individual-patient level for specific disorders, among which non suicidal self-injury. We aimed at applying classification trees to predict non-suicidal self-injury (NSSI) with EMA data collected through a mobile app. A database of 40 patients diagnosed with borderline personality disorder (BPD) with NSSI (N=22), and a subclinical group of students with NSSI (N=19) was analysed. EMA data was collected by the Sinjur app. Two classification trees were used as models. For the first tree, training results reported 69,7% of accuracy, whereas test results reported 59,3% of accuracy, 87,5% of sensitivity and 58,78% of specificity. For the second tree, training results reported 67,9% of accuracy, whereas test results reported 65,2% of accuracy, 85% of sensitivity and 64,8% of specificity. We concluded that real-time patient monitoring via a mobile app can be a valuable tool for making technology-based predictions at the individual patient level. This promising data needs to be built upon in future studies and needs major translation in the everyday clinical practice to demonstrate its real-world efficacy and later, to be translated to the enterprise world.

AveRobot: An Audio-visual Dataset for People Re-identification and Verification in Human-Robot Interaction

Topics: Biometrics; Robotics; Image and Video Analysis and Understanding

Authors: Mirko Marras ¹ , Pedro A. Marín-Reyes ² , Javier Lorenzo-Navarro ² , Modesto Castrillón-Santana ² and Gianni Fenu ¹

Affiliations: ¹ Department of Mathematics and Computer Science, University of Cagliari, V. Ospedale 72, 09124 Cagliari and Italy , ² Instituto Universitario Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (SIANI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria and Spain

Digital Object Identifier: 10.5220/0007690902550265

Proceedings of ICPRAM 2019, ISBN: 978-989-758-351-3

Quick Abstract

Intelligent technologies have pervaded our daily life, making it easier for people to complete their activities. One emerging application is involving the use of robots for assisting people in various tasks (e.g., visiting a museum). In this context, it is crucial to enable robots to correctly identify people. Existing robots often use facial information to establish the identity of a person of interest. But, the face alone may not offer enough relevant information due to variations in pose, illumination, resolution and recording distance. Other biometric modalities like the voice can improve the recognition performance in these conditions. However, the existing datasets in robotic scenarios usually do not include the audio cue and tend to suffer from one or more limitations: most of them are acquired under controlled conditions, limited in number of identities or samples per user, collected by the same recording device, and/or not freely available. In this paper, we propose AveRobot, an audio-visual dataset of 111 participants vocalizing short sentences under robot assistance scenarios. The collection took place into a three-floor building through eight different cameras with built-in microphones. The performance for face and voice re-identification and verification was evaluated on this dataset with deep learning baselines, and compared against audio-visual datasets from diverse scenarios. The results showed that AveRobot is a challenging dataset for people re-identification and verification.

Related Ontology Subjects/Areas/Topics (11)

Applications	Biometrics and Pattern Recognition	Robotics
Biomedical Engineering	Multimedia	Software Engineering
Biomedical Signal Processing	Multimedia Signal Processing	Telecommunications
Biometrics	Pattern Recognition

A Preliminary Review of Behavioural Biometrics for Health Monitoring in the Elderly

Authors: Jordi Solé-Casals , Mihaela Vancea and Jaume Miquel March

Affiliation: University of Vic - Central University of Catalonia, Spain

Digital Object Identifier: 10.5220/0005321603650371

Proceedings of MPBS 2015/BIOSTEC, ISBN: 978-989-758-069-7

Quick Abstract

This article explores the potential of ICT-based biometrics for monitoring the health status of the elderly people. It departs from specific ageing and biometric traits to then focus on behavioural biometric traits like handwriting, speech and gait to finally explore their practical application in health monitoring of elderly.

Managing and 3D Visualization of Real-time Big Geo-referenced Data from Las Palmas Port through a Flexible Open Source Computer Architecture

Topics: Web Applications; Big Data and GIS

Authors: José P. Suárez , Agustín Trujillo , Conrado Domínguez , José M. Santana and Pablo Fernández

Affiliation: University of Las Palmas de Gran Canaria, Spain

Digital Object Identifier: 10.5220/0005371700720082

Proceedings of GISTAM 2015, ISBN: 978-989-758-099-4

Quick Abstract

Nowadays, new technologies assist the capture and analysis of data for all kinds of organizations. A good example of this trend are the seaports that generate data regarding the management of marine traffic and other elements, as well as environmental conditions given by meteorological sensors and buoys. However, this enormous amount of data, also known as “Big Data”, is useless without a proper system to visualize and organize them. Governments are fully aware of this and promote the creation of visualization and control systems that are useful to port authorities. In the line of management systems based on GIS, the SmartPort project has been developed. SmartPort offers a rich-internet application that allows the user to visualize and manage the different sources of information of a port environment. The “Big Data” management is based on the FI-WARE tools and architecture, as well as “The Internet of Things” solutions for the data acquisition. At the same time, the Glob3 Mobile SDK for the development of map apps will support the 3D visualization of the port’s scenery and its data sources.

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