No front page content has been created yet.

"Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals" paper published in Frontiers in Neuroinformatics journal

In this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

"An IoT-based contribution to improve mobility of the visually impaired in Smart Cities" paper published in Computing journal (Springer)

In this work, an integrated framework with an IoT architecture customized for an electronic cane (electronic travel aid designed for the visually impaired) has been designed. The architecture is organized by a five-layer architecture: edge technology, gateway, Internet, middleware and application. This new feature brings the ability to connect to environment devices, receiving the coordinates of their geographic locations, alerting the user when it is close to anyone of these devices and sending those coordinates to a web application for smart monitoring. Preliminary studies and experimental tests with three blind users of the Cane show that this approach would contribute to get more spatial information from the environment improving mobility of visually impaired people.

"Deep-Sync: A novel deep learning-based tool for semantic-aware subtitling synchronisation" paper published in "Neural Computing and Applications" journal

In this paper, we present Deep-Sync, a tool for the alignment of subtitles with the audio-visual content. The architecture integrates a deep language representation model and a real-time voice recognition software to build a semantic-aware alignment tool that successfully aligns most of the subtitles even when there is no direct correspondence between the re-speaker and the audio content. Deep-Sync was compared with other subtitles alignment tool, showing that our proposal is able to improve the synchronisation in all tested cases.

Plataforma EASIER, una ayuda en la comprensión de los textos

EASIER es una plataforma que ayuda a las personas a comprender mejor los textos y funciona apoyándose en métodos de inteligencia artificial. Básicamente proporciona simplificación léxica de los textos en español ofreciendo distintas ayudas a la comprensión.