Framework for the development of articulatory characterization studies over mri images

En este artículo se presenta un marco de trabajo tecnológico innovador diseñado y desarrollado por nuestro grupo de investigación para posibilitar la realización de estudios de caracterización articulatoria de los sonidos de una lengua a partir de medidas tomadas sobre secuencias de imágenes de cine-MRI. Como elemento fundamental se tiene la herramienta software de producción propia DicomPas, que permite realizar la toma de medidas de parámetros articulatorios sobre las secuencias de imágenes MRI y la ejecución de algoritmos ad hoc sobre dichas medidas, de cara al procesamiento de los datos, con vistas a la posterior extracción del conocimiento, en forma de generación de inferencias estadísticas o de inteligencia artificial. En estos momentos este marco de trabajo está siendo aplicado a la realización de diversos estudios en euskara y español de Euskadi, disponiéndose para ello de una base de datos con dos repositorios de imágenes tomadas en el plano medio sagital, correspondientes a 18 informantes diferentes.In this paper an innovative framework is presented, designed and developed by our research team to enable the accomplishment of research works concerning the articulatory characterization of the sounds of a language from measures taken over MRI image sequences. As fundamental element there is the DicomPas software tool, developed by our team, which allows to carry out the measures of articulatory parameters over the MRI image sequences and the execution of ad hoc algorithms over such measures, facing the data processing, with the view to the subsequent extraction of knowledge, in the form of the generation of statistical or artificial intelligence inferences. This framework is currently being applied to the achievement of diverse studies in Basque and Spanish of the Basque Country. To do so, a database with two repositories of images taken in the midsagittal plane, corresponding to 18 different informants, is available

Nueva metodología de enseñanza de procesado digital de la señal utilizando la API “joPAS”

Este artículo presenta la API de programación “JoPAS” desarrollada por el grupo de investigación PAS de la universidad de Deusto. joPAS permite el uso de variables y funciones de Octave desde un programa realizado en Java. Esta API posibilita a los estudiantes el rápido desarrollo de aplicaciones de procesado digital de señal, haciendo uso de la sencillez de diseño y potencia de interfaces gráficas en leguaje Java y el cálculo científico en Octave. Esta nueva herramienta docente está siendo utilizada por alumnos de ingeniería informática e ingeniería técnica de telecomunicación

Algorithms Based on CWT and Classifiers to Control Cardiac Alterations and Stress Using an ECG and a SCR

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A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee

Sometimes, one needs to control different emotional situations which can lead the person suffering them to dangerous situations, in both the medium and short term. There are studies which indicate that stress increases the risk of cardiac problems. In this study we have designed and built a stress sensor based on Galvanic Skin Response (GSR), and controlled by ZigBee. In order to check the device’s performance, we have used 16 adults (eight women and eight men) who completed different tests requiring a certain degree of effort, such as mathematical operations or breathing deeply. On completion, we appreciated that GSR is able to detect the different states of each user with a success rate of 76.56%. In the future, we plan to create an algorithm which is able to differentiate between each state

Kinect-based virtual game for motor and cognitive rehabilitation: A pilot study for older adults

ABSTRACT Physical rehabilitation is often necessary for individuals who suffer an injury or illness which causes a physical impairment, in order to restore movement and strength through supervised repetitive exercises. Alternatively, physical activity also improve cognitive performance and reduce cognitive decline. This tool focuses on therapeutic aspects of both cognitive and physical rehabilitation of older adults, that is, it improves the memory by performing mental activities and physical rehabilitation at the same time. To achieve this, a Kinect based virtual game intended for Windows which enables users to control and interact intuitively with the computer without an intermediary controller has been developed. Furthermore, all the data generated during the session is stored in order to log every rehabilitation activity. Preliminary tests have shown an increase in the users' motivation while using the tool and it assessed the possible rehabilitation of 14 patients with motor impairments (p < 0.05) and the maintenance of their cognitive impairment avoiding its degradation

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

Lifelong learning enables professionals to update their skills to face challenges in their changing work environments. In view of the wide range of courses on offer, it is important for professionals to have recommendation systems that can link them to suitable courses. Based on this premise and on our previous research, this paper proposes the use of ontology to model job sectors and areas of knowledge, and to represent professional skills that can be automatically updated using the profiled data and machine learning for clustering entities. A three-stage hybrid system is proposed for the recommendation process: semantic filtering, content filtering and heuristics. The proposed system was evaluated with a set of more than 100 user profiles that were used in a previous version of the proposed recommendation system, which allowed the two systems to be compared. The proposed recommender showed 15% improvement when using ontology and clustering with DBSCAN in recall and serendipity metrics, and a six-point increase in harmonic mean over the stored data-based recommender system

Non invasive techniques for direct muscle quality assessment after exercise intervention in older adults: a systematic review

Abstract Background The aging process induces neural and morphological changes in the human musculoskeletal system, leading to a decline in muscle mass, strength and quality. These alterations, coupled with shifts in muscle metabolism, underscore the essential role of physical exercise in maintaining and improving muscle quality in older adults. Muscle quality's morphological domain encompasses direct assessments of muscle microscopic and macroscopic aspects of muscle architecture and composition. Various tools exist to estimate muscle quality, each with specific technical requirements. However, due to the heterogeneity in both the studied population and study methodologies, there is a gap in the establishment of reference standards to determine which are the non-invasive and direct tools to assess muscle quality after exercise interventions. Therefore, the purpose of this review is to obtain an overview of the non-invasive tools used to measure muscle quality directly after exercise interventions in healthy older adults, as well as to assess the effects of exercise on muscle quality. Main text To address the imperative of understanding and optimizing muscle quality in aging individuals, this review provides an overview of non-invasive tools employed to measure muscle quality directly after exercise interventions in healthy older adults, along with an assessment of the effects of exercise on muscle quality. Results Thirty four studies were included. Several methods of direct muscle quality assessment were identified. Notably, 2 studies harnessed CT, 20 utilized US, 9 employed MRI, 2 opted for TMG, 2 adopted myotonometry, and 1 incorporated BIA, with several studies employing multiple tests. Exploring interventions, 26 studies focus on resistance exercise, 4 on aerobic training, and 5 on concurrent training. Conclusions There is significant diversity in the methods of direct assessment of muscle quality, mainly using ultrasound and magnetic resonance imaging; and a consistent positive trend in exercise interventions, indicating their efficacy in improving or preserving muscle quality. However, the lack of standardized assessment criteria poses a challenge given the diversity within the studied population and variations in methodologies.. These data emphasize the need to standardize assessment criteria and underscore the potential benefits of exercise interventions aimed at optimizing muscle quality

Correction: Grenez, F., et al. Wireless Prototype Based on Pressure and Bending Sensors for Measuring Gait Quality. Sensors 2013, 13, 9679–9703

In [1], we would like to change “Gate” to “Gait” in the title, which should read “Prototype Based on Pressure and Bending Sensors for Measuring Gait Quality”. In Figure 7 we would like to change the analog inputs. The measurements should be between the sensor and the resistance, and not after the resistance. The revised figure is shown below