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The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use. [ABSTRACT FROM AUTHOR]

This paper studies and assesses how rehabilitation activities and supervised computer games incorporated into a system aimed at people diagnosed with Autism Spectrum Disorder (ASD) can be used to work on the areas affected by ASD at any time and in any place. This research specifically assesses the areas that affect communication and interaction between people with ASD and professionals. In order to do this, a group of 20 children diagnosed with ASD of between 3 and 8 years old (clinical group) was used, together with a group of 20 children of between 3 and 8 years old with a neurotypical development, which served as a control group. During the tests, response time and visual interaction with the session leader were evaluated. Despite the fact that the clinical group spent more time (M= 21.08 sec) than the control group (M= 4.52) to interact leader, eye contact predominated in the interaction. As a result of the pilot study, the system obtained could help in cognitive rehabilitation. [ABSTRACT FROM AUTHOR]

This paper presents the results of research that applies cognitive therapies associated with memory and mathematical problem-solving in elderly people. The exercises are programmed in an iPad and can be performed both from the Tablet and in an interactive format with a LEGO robot. The system has been tested with 2 men and 7 women over the age of 65 who have slight physical and cognitive impairment. Evaluation with the SUS resulted in a mean of 48.45 with a standard deviation of 5.82. The score of overall satisfaction was 84.37 with a standard deviation of 18.6. Interaction with the touch screen caused some usability problems due to the elderly people's visual difficulties and clicking accuracy. Future versions will include visualization with more color contrast and less use of the keyboard. [ABSTRACT FROM AUTHOR]

People with Multiple Sclerosis (MS) need regular physical activities along with medical treatment despite their ability or disability level. However, poorly performed exercises could aggravate muscle imbalances and worsen their health. The goal of our work is to create a comprehensive system, encompassing a face-to-face sessions performed by MS patients one day a week at the medical center with exercises at home the rest of the week through a web platform in combination with a tracking tool to analyze the position of patients during exercise and correct them in real-time. The whole system is currently testing during six months with ten participants, five persons with MS and 5 professionals related with MS. Two tests, the Functional Independence Measure and the Berg Balance Scale will be act as a barometer for measuring the degree of independence obtained by the people with MS and also the validity of the whole system as a rehabilitation tool. Preliminary results about the usability of the system using SUS scale, 72 and 76 points over 100 (patients and professionals respectively), demonstrate that our system is usable for both patients and professionals. [ABSTRACT FROM AUTHOR]

Graph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used. [ABSTRACT FROM AUTHOR]