Design and Evaluation of a Vision-Based UI for People with Large Cognitive-Motor Disabilities

Recovering from multiple traumatic brain injury (TBI) is a very difficult task, depending on the severity of the lesions, the affected parts of the brain and the level of damage (locomotor, cognitive or sensory). Although there are some software platforms to help these patients to recover part of the lost capacity, the variety of existing lesions and the different degree to which they affect the patient, do not allow the generalization of the appropriate treatments and tools in each case. The aim of this work is to design and evaluate a machine vision-based UI (User Interface) allowing patients with a high level of injury to interact with a computer. This UI will be a tool for the therapy they follow and a way to communicate with their environment. The interface provides a set of specific activities, developed in collaboration with the multidisciplinary team that is currently evaluating each patient, to be used as a part of the therapy they receive. The system has been successfully tested with two patients whose degree of disability prevents them from using other types of platforms

Aerial obstacle detection with 3D mobile devices

In this paper, we present a novel approach for aerial obstacle detection (e.g. branches or awnings) using a 3D smartphone in the context of the visually impaired (VI) people assistance. This kind of obstacles are especially challenging because they cannot be detected by the walking stick or the guide dog. The algorithm captures the 3D data of the scene through stereo vision. To our knowledge, this is the first work that presents a technology able to obtain real 3D measures with smartphones in real time. The orientation sensors of the device (magnetometer and accelerometer) are used to approximate the walking direction of the user, in order to look for the obstacles only in such direction. The obtained 3D data are compressed and then linearized for detecting the potential obstacles. Potential obstacles are tracked in order to accumulate enough evidence to alert the user only when a real obstacle is found. In the experimental section, we show the results of the algorithm in several situations using real data and helped by VI users.J.M. Sáez and M.A. Lozano are supported by the University of Alicante research grant GRE10-21. F. Escolano is supported by the project TIN2012-32839 of the Spanish Government

Minimization of measuring points for the electric field exposure map generation in indoor environments by means of Kriging interpolation and selective sampling

In a world with increasing systems accessing to radio spectrum, the concern for exposure to electromagnetic fields is growing and therefore it is necessary to check limits in those areas where electromagnetic sources are working. Therefore, radio and exposure maps are continuously being generated, mainly in outdoor areas, by using many interpolation techniques. In this work, Surfer software and Kriging interpolation have been used for the first time to generate an indoor exposure map. A regular measuring mesh has been generated. Elimination of Less Significant Points (ELSP) and Geometrical Elimination of Neighbors (GEN) strategies to reduce the measuring points have been presented and evaluated. Both strategies have been compared to the map generated with all the measurements by calculating the root mean square and mean absolute errors. Results indicate that ELSP method can reduce up to 70% of the mesh measuring points while producing similar exposure maps to the one generated with all the measuring points. GEN, however, produces distorted maps and much higher error indicators even for 50% of eliminated measuring points. As a conclusion, a procedure for reducing the measuring points to generate radio and exposure maps is proposed based on the ELSP method and the Kriging interpolation.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Frequency and Longitudinal Course of Motor Signs in Genetic Frontotemporal Dementia

Background and Objectives Frontotemporal dementia (FTD) is a highly heritable disorder. The majority of genetic cases are caused by autosomal dominant pathogenic variants in the chromosome 9 open reading frame 72 (c9orf72), progranulin (GRN), and microtubule-associated protein tau (MAPT) gene. As motor disorders are increasingly recognized as part of the clinical spectrum, the current study aimed to describe motor phenotypes caused by genetic FTD, quantify their temporal association, and investigate their regional association with brain atrophy. Methods We analyzed baseline visit data of known carriers of a pathogenic variant in the c9orf72, GRN, or MAPT gene from the Genetic Frontotemporal Dementia Initiative cohort study. Principal component analysis with varimax rotation was performed to identify motor sign clusters that were compared with respect to frequency and severity between groups. Associations with cross-sectional atrophy patterns were determined using voxel-wise regression. We applied linear mixed effects models to assess whether groups differed in the association between motor signs and estimated time to symptom onset. Results A total of 322 pathogenic variant carriers were included in the analysis: 122 c9orf72 (79 presymptomatic), 143 GRN (112 presymptomatic), and 57 MAPT (43 presymptomatic) pathogenic variant carriers. Principal component analysis revealed 5 motor clusters, which we call progressive supranuclear palsy (PSP)-like, bulbar amyotrophic lateral sclerosis (ALS)-like, mixed/ALS-like, Parkinson disease (PD) like, and corticobasal syndrome–like motor phenotypes. There was no significant group difference in the frequency of signs of different motor phenotypes. However, mixed/ALS-like motor signs were most frequent, followed by PD-like motor signs. Although the PSP-like phenotype was associated with mesencephalic atrophy, the mixed/ALS-like phenotype was associated with motor cortex and corticospinal tract atrophy. The PD-like phenotype was associated with widespread cortical and subcortical atrophy. Estimated time to onset, genetic group and their interaction influenced motor signs. In c9orf72 pathogenic variant carriers, motor signs could be detected up to 25 years before expected symptom onset. Discussion These results indicate the presence of multiple natural clusters of motor signs in genetic FTD, each correlated with specific atrophy patterns. Their motor severity depends on time and the affected gene. These clinicogenetic associations can guide diagnostic evaluations and the design of clinical trials for new disease-modifying and preventive treatments.UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/M023664/1German Research Foundation (DFG) EXC 2145 390857198Ministry of Health, ItalyCanadian Institutes of Health Research (CIHR)JPND grant GENFIproxEuropean Reference Network for Rare Neurological Diseases (ERN-RND) European Commission European Commission Joint Research Centre ERNRND: 3HP 767231Ministerio de Ciencia e Innovacion (Espana)/FEDER RTI2018-098913-B100Junta de AndaluciaEuropean Commission CV20-45250 A-TIC080-UGR18Canadian Institutes of Health Research (CIHR) MOP 327387Weston Brain InstituteUK Research & Innovation (UKRI)Medical Research Council UK (MRC)European Commission SUAG/051 G101400National Institute for Health Research (NIHR) BRC-1215-2001

Robótica para las Ingenierías en Informática en la Universidad de Alicante

En este artículo describimos una propuesta para los contenidos, tanto teóricos como prácticos de la asignatura Robótica en Ingeniería en Informática. Dicha propuesta está implementada actualmente sobre dicha titulación en la Universidad de Alicante

Self-Esteem and Coping Strategies in Adolescent Cancer Patients during the Period of Illness and Follow-Up

The importance of self-esteem during the course of oncological illness has been well-documented by some previous studies. However, data assessing its association with various coping strategies, especially considering the period of illness, are still scarce. The objective of this study is to analyze the differences in coping strategies among oncological adolescents, taking into account their self-esteem, illness period, age, and sex. A total of 201 oncological patients between the ages of 12 and 17 from three different Spanish cities were included in this study. All of them were asked to answer a tailored questionnaire, encompassing information about age, sex, and illness period. Additionally, the coping strategies were measured using the ACS scale, while self-esteem was evaluated using the SENA questionnaire. The results demonstrated that male adolescents and older individuals exhibited higher levels of self-esteem. The main coping strategies associated with higher self-esteem were “ignore the problem”, “focus on positive”, “physical recreation”, and “wishful thinking” both during the treatment and the follow-up phases. We conclude that higher self-esteem is associated with some of the coping strategies such as “focus on positive”, “ignore the problem”, and “wishful thinking”. Sociodemographic variables influence the relationship between self-esteem and coping strategies, but no differences were found regarding the period of illness

A Spherical Brain Mapping of MR Images for the Detection of Alzheimer's Disease

Magnetic Resonance Imaging (MRI) is of fundamental importance in neuroscience, providing good contrast and resolution, as well as not being considered invasive. Despite the development of newer techniques involving radiopharmaceuticals, it is still a recommended tool in Alzheimer’s Disease (AD) neurological practice to assess neurodegeneration, and recent research suggests that it could reveal changes in the brain even before the symptomatology appears. In this paper we propose a method that performs a Spherical Brain Mapping, using different measures to project the three-dimensional MR brain images onto two-dimensional maps revealing statistical characteristics of the tissue. The resulting maps could be assessed visually, but also perform a significant feature reduction that will allow further supervised or unsupervised processing, reducing the computational load while maintaining a large amount of the original information. We have tested our methodology against a MRI database comprising 180 AD affected patients and 180 normal controls, where some of the mappings have revealed as an optimum strategy for the automatic processing and characterization of AD patterns, achieving up to a 90.9% of accuracy, as well as significantly reducing the computational load. Additionally, our maps allow the visual analysis and interpretation of the images, which can be of great help in the diagnosis of this and other types of dementia.This work was partly supported by the MICINN under the TEC2008-02113 and TEC2012-34306 projects and the Consejerıa de Econom´ıa, Innovacion, Ciencia y Empleo (Junta de Andalucıa, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103, as well as the “Programa de fortalecimiento de las capacidades de I+D+I en las Universidades 2014-2015”, cofunded by the European Regional Development Fund (ERDF) under Project FC14-SAF-3

Deep Residual Transfer Learning for Automatic Diabetic Retinopathy Grading.

Evaluation and diagnosis of retina pathology is usually made via the analysis of different image modalities that allow to explore its structure. The most popular retina image method is retinography, a technique that displays the fundus of the eye, including the retina and other structures. Retinography is the most common imaging method to diagnose retina diseases such as Diabetic Retinopathy (DB) or Macular Edema (ME). However, retinography evaluation to score the image according to the disease grade presents difficulties due to differences in contrast, brightness and the presence of artifacts. Therefore, it is mainly done via manual analysis; a time consuming task that requires a trained clinician to examine and evaluate the images. In this paper, we present a computer aided diagnosis tool that takes advantage of the performance provided by deep learning architectures for image analysis. Our proposal is based on a deep residual convolutional neural network for extracting discriminatory features with no prior complex image transformations to enhance the image quality or to highlight specific structures. Moreover, we used the transfer learning paradigm to reuse layers from deep neural networks previously trained on the ImageNet dataset, under the hypothesis that first layers capture abstract features than can be reused for different problems. Experiments using different convolutional architectures have been carried out and their performance has been evaluated on the MESSIDOR database using cross-validation. Best results were found using a ResNet50-based architecture, showing an AUC of 0.93 for grades 0 + 1, AUC of 0.81 for grade 2 and AUC of 0.92 for grade 3 labelling, as well as AUCs higher than 0.97 when considering a binary classification problem (grades 0 vs 3).This work was partly supported by the MINECO/FEDER under TEC2015-64718-R, RTI2018-098913-B-I00, PSI2015-65848-R and PGC2018-098813-B-C32 projects. We gratefully acknowledge the support of NVIDIA Cor poration with the donation of one of the GPUs used for this research. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Formacion” Fellowship

Anthropometric Dimensions and Bone Quality in International Male Beach Handball Players: Junior vs. Senior Comparison

Background: Beach handball is a recent team sport characterized by defensive and offensive actions on a sand surface. Scientific evidence has shown that body composition is fundamental in sports performance. The main objective of this study was to know the body composition, anthropometric characteristics, and bone mineral density of elite beach handball players. Furthermore, another purpose was to analyze the differences between categories (junior and senior) and playing position. Methods: A descriptive, cross-sectional study of 36 male players (18 juniors and 18 seniors) of the Spanish National Beach Handball Team was conducted. Full profile anthropometry and calcaneal ultrasound measurements were used. Results: Significant differences between categories (p < 0.05) were found in: height, body mass, arm span, BMI, muscle mass, fat mass, bone mass, skinfolds, and body perimeters. The somatotype changes depending on the playing position. Bone mineral density of the players was adequate. No significant differences were found by playing position. Conclusions: Senior players had a better body composition due to the presence of less fat mass than junior players. This study provides reference values of elite junior and senior beach handball players and by playing positions. This data is useful for the identification of talents and players who should be trained to improve their body composition

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/ FEDER under TEC2015-64718-R and PSI2015- 65848-R projects and the Consejer´ıa de Innovaci´on, Ciencia y Empresa (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103 as well as the Salvador deMadariaga Mobility Grants 2017. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; P zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]