An Automated Fall Detection System Using Recurrent Neural Networks

Falls are the most common cause of fatal injuries in elderly people, causing even death if there is no immediate assistance. Fall detection systems can be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional falls by analyzing these signals. These algorithms have proven to achieve high effectiveness with competitive performances in many classification problems. This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on accelerometer data, reducing computational requirements of previous research. The architectures have been tested on a labeled version of the publicly available SisFall dataset, achieving a mean F1-score above 0.73 and improving state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

Parental style, parental self-efficacy and children behavioral problems in three municipalities of Santander

Introduction: The family, through child rearing, has a key role in the development of children’s social skills and self-care. Objective: To describe parental styles, parental self-efficacy and perception of children behavior’s problems among parents and caregivers, and explore differences by sex and place of residence in three municipalities of Santander. Material and Method: Quantitative, observational, descriptive cross-sectional study. Participants were parents and caregivers of children under 12 years old, living in the municipalities of Girón, Lebrija, and Matanza. The Strengths and Difficulties Questionnaire, the Parenting Scale and the Parenting Tasks Checklist were used. Comparisons were made by sex, municipality, and zone (urban/rural). Results: Participants were 1425 parents and caregivers of children under 12 years old. Most participants were female (80%) and mothers (70%). High prevalence of dysfunctional parental styles was identified with predominance of over reactivity (36%), laxness (23%) and hostility (22%). Higher percentage of difficulties with parent’s contextual self-efficacy was identified in the rural area (p<0,001). Conclusions: This study found dysfunctional parental styles and perception of children’s behavioral problems that suggest the need for population-based interventions focused on parental skills for parents and caregivers. Child rearing is proposed as a process of interest in public health. Introducción: La familia, a partir de la crianza, juega un papel fundamental en el desarrollo de habilidades sociales y de autocuidado de las personas. Objetivo: Describir los estilos de crianza, la autoeficacia parental y la percepción de problemas de la conducta infantil por parte de padres y cuidadores de niños, además, explorar diferencias por sexo y zona de residencia en tres municipios de Santander. Materiales y método: Estudio cuantitativo, observacional, descriptivo de corte transversal. Se incluyeron padres y cuidadores adultos de niños menores de 12 años residentes en los municipios de Girón, Lebrija y Matanza. Se aplicaron el cuestionario de capacidades y dificultades, la escala de estilo parental y la lista de verificación de las tareas parentales. Se realizaron comparaciones por sexo, municipio y zona (urbana/rural). Resultados: Participaron 1.425 adultos cuidadores de niños menores de 12 años. La mayoría de los participantes fueron mujeres (80%) y su rol era madre (70%). Se identificó una prevalencia alta de estilos de crianza disfuncionales caracterizados por la sobrerreactividad (36%), la laxitud (23%) y la hostilidad (22%). En la zona rural se encontró una mayor proporción de falta de autoeficacia contextual (p<0,001). Conclusiones: Se evidencia la presencia de estilos de crianza disfuncionales y alteraciones comportamentales de los niños que sugieren la necesidad de intervenciones poblacionales intersectoriales a padres y cuidadores. Se propone posicionar la crianza como un proceso de interés en salud pública

Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI

Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are becoming popular in medical applications and image-based diagnosis. Convolutional Neural Networks are the preferred architecture for object detection and classification in images. In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using Fuzzy C-Means.Ministerio de Economía y Competitividad TEC2016-77785-

Sampling Frequency Evaluation on Recurrent Neural Networks Architectures for IoT Real-time Fall Detection Devices

Falls are one of the most frequent causes of injuries in elderly people. Wearable Fall Detection Systems provided a ubiquitous tool for monitoring and alert when these events happen. Recurrent Neural Networks (RNN) are algorithms that demonstrates a great accuracy in some problems analyzing sequential inputs, such as temporal signal values. However, their computational complexity are an obstacle for the implementation in IoT devices. This work shows a performance analysis of a set of RNN architectures when trained with data obtained using different sampling frequencies. These architectures were trained to detect both fall and fall hazards by using accelerometers and were tested with 10-fold cross validation, using the F1-score metric. The results obtained show that sampling with a frequency of 25Hz does not affect the effectiveness, based on the F1-score, which implies a substantial increase in the performance in terms of computational cost. The architectures with two RNN layers and without a first dense layer had slightly better results than the smallest architectures. In future works, the best architectures obtained will be integrated in an IoT solution to determine the effectiveness empirically.Ministerio de Economía y Competitividad TEC2016-77785-

Algoritmo de Encriptación de Imágenes Utilizando el Atractor Caótico de Lorenz

Context: With the increasing volumes of information generated in real time, novel mechanisms area needed to ensure security so as to prevent access to unauthorized people. The conventional encryption methods are not appropriate for images, because they are prone to statistical attacks due to the strong correlation between adjacent pixels and the analysis of color gamut histograms, which can help to identify them within the image; with this aim in mind, in this paper an algorithm for image encryption using chaotic attractors is proposed.Method: Chaotic synchronization is used for the key management, the diffusion stage is made by means of ergodic sequences generated from the numerical solution of the Lorenz’s attractor and the permutation stage is accomplished with the wave line technique.Results: The proposed algorithm was tested using a set of gray-scale images obtaining suitable performance in segurity and speed, the pixel correlation is almost null and the entropy is similar to that presented in recent works with the same approach.Conclusions: Chaotic methods are an alternative to improve the security levels in the cryptography of images due to their properties of unpredictability and sensitivity to the initial conditions. For future work the approach presented could be applied to the encryption of color images and using different chaotic attractors.Contexto: Con el creciente volumen de  información generada en tiempo real, nuevos mecanismos son necesarios para garantizar su seguridad, evitando el acceso a personas no autorizadas. Los métodos convencionales de criptografía no son apropiados para imágenes por su debilidad a ataques estadísticos, debido a la fuerte correlación entre píxeles adyacentes y el análisis de los histogramas de gamas de colores, lo que puede ayudar a identificarlos dentro de la imagen; con este objetivo en mente, se propone un algoritmo para el cifrado de imágenes utilizando atractores caóticos.Método: La sincronización caótica se utiliza para la gestión de la clave, la etapa de difusión se realiza mediante secuencias ergódicas generadas a partir de la solución numérica del atractor de Lorenz y la etapa de permutación se realiza con la técnica de línea de onda.Resultados: Se probó el algoritmo propuesto utilizando un conjunto de imágenes en escala de grises, obteniendo un desempeño adecuado en seguridad y velocidad, la correlación de píxeles es casi nula y la entropía es similar a la presentada en trabajos recientes con el mismo enfoque.Conclusiones: Los métodos caóticos son una buena alternativa para mejorar los niveles de seguridad en la criptografía de imágenes debido a sus propiedades de imprevisibilidad y sensibilidad a las condiciones iniciales. Para trabajos futuros el enfoque presentado podría aplicarse a la encriptación de imágenes a color y utilizando diferentes atractores caóticos

Localización e identificación automática de pólipos mediante una red neuronal convolucional por regiones

Este trabajo expone la metodología llevada a cabo para la aplicación de un modelo Deep Learning con el fin de detectar pólipos de forma automática, así como su posición en videos de colonoscopia. Se plantearon diferentes métodos y diversas técnicas que pudieran aplicarse sobre el conjunto de datos proporcionado por el 2018 Sub-challenge Gastrointestinal Image ANAlysis. Seleccionamos el método Faster Regional Convolutional Neural Network para abarcar el problema planteado. Para la extracción de características empleamos el modelo ResNet50. Aplicamos técnicas de data augmentation para incrementar el conjunto de datos empleado en el entrenamiento del modelo. También aplicamos hard negative mining para reforzar el aprendizaje del background o fondo, reducir el porcentaje de falsos positivos y mejorar el rendimiento.This work exposes the methodology carried out for the application of a Deep Learning model in the context of automatic polyp detection and its location in colonoscopy videos. Different methods were proposed as well as the different techniques that can be applied on the given dataset provided by the 2018 Sub-challenge Gastrointestinal Image ANAlysis. We chose the Faster Regional Convolutional Neural Network method to solve this problem. We used ResNet50 in the first part of this algorithm to extract the main image features. We applied hard negative mining and data augmentation techniques to increase the dataset used in the training of the model. We also used hard negative mining to get a better learning of background, reducing false negatives and improving the performance

Clasificación de tumores en cáncer de mama basado en redes neuronales de convolución

El cáncer de mama es una de las causas más frecuentes de mortalidad en las mujeres. Con la llegada de los sistemas inteligentes, la detección automática de tumores en mamografías se ha convertido en un gran reto y puede jugar un papel crucial para mejorar el diagnóstico médico. En este trabajo, se propone un sistema de diagnóstico asistido por ordenador basado en técnicas de Deep Learning, específicamente en redes neuronales de convolución (CNN). El sistema está dividido en dos partes: en primer lugar, se realiza un preprocesamiento sobre las mamografías extraídas de una base de datos pública; posteriormente, las CNNs extraen características de las imágenes preprocesadas para finalmente clasificarlas en función de los dos tipos de tumores existentes: benignos y malignos. Los resultados de este estudio muestran que el sistema tiene una precisión del 80% en clasificación de tumores.Breast cancer is one of the most frequent causes of mortality in women. With the arrival of the artificial intelligent, the automatic detection of tumors in mammograms has become a big challenge and can play a crucial role in improving medical diagnosis. In this work, a computer-aided diagnosis system based on Deep Learning techniques, specifically in Convolutional Neural Networks (CNN), is proposed. The system is divided into two parts: first, a preprocessing is performed on mammograms taken from a public database; then, the CNN extracts features of the preprocessed images to finally classify them accordingly to the type of tissue. The results of this study show that the system has an accuracy of 80% in the classification

Deep learning as a tool for improving efficiency the of glial tumor diagnosis

La aplicación de técnicas basadas en Inteligencia Artificial como apoyo a la detección y diagnosis de cáncer mediante imagen es una práctica muy extendida hoy día. Además, el reconocimiento por regiones de interés y otros algoritmos constituyen una rama de investigación amplia que mejoran considerablemente la calidad de la clasificación. En este trabajo, se propone como caso de estudio la identificación del tumor glial con Imágenes por Resonancia Magnética de pacientes sanos y enfermos mediante la combinación de algoritmos de Deep Learning de detección de regiones que se basan en la extracción de regiones de interés en la imagen utilizando una red Spatial Pyramid Pooling combinado con la modificación de las imágenes de entrada con el algoritmo Fuzzy c-means. Obteniendo un acierto cercano al del personal sanitario.Nowadays, existing Artificial Intelligent techniques are used as a support for cancer detection and diagnosis. Moreover, regional object interest and other related algorithms have become common place for improving the quality of the classification. Opening a wide field of interest and research. In this work, a deep neural network based on a new pooling strategy and image segmentation (Fuzzy c-means) [1] is proposed for glial tumor in Magnetic Resonance Imaging (MRI) images by using Region of Interests methods as Spatial Pyramid Pooling. The power of SPP-net is the possibility of working with feature maps from images with different sizes, and then subsampling these features to generate a fixed-length set and to implement finally a classification step

¡Aprendamos jugando!: la yincana como recurso didáctico y motivador

En esta comunicación presentamos la experiencia llevada a cabo por un grupo de estudiantes internos del Departamento de Didáctica de las Ciencias Experimentales y Sociales de la Universidad de Sevilla que estudian el Grado en Educación Primaria. La experiencia llevada cabo ha sido la organización y puesta en marcha de una yincana con alumnado de 3º del CEIP Olivar de Quinto. El objetivo de la yincana es didáctico tanto para el alumnado de Primaria como para el universitario. Con este tipo de acciones de carácter lúdico, se pretenden trabajar los contenidos de forma integral. Además para los universitarios, es una oportunidad de acercarse a la realidad de un centro escolar, y realizar actividades con un carácter más innovador