The use of digital filters as the Wavelet Transform is widely recognized in signal processing; however, for the analysis of an electroencephalographic (EEG) signal, the most optimal filter to be used has not been definitively determined. This work presents a comparison between the results obtained by filtering an EEG signal recorded during an 8 minute foreign language class on 69 asymptomatic volunteers using Wavelet Symlets (sym2 - sym10) and Daubechies (db2 - db10). The EEG signals were divided into four sub-bands and an energy, frequency and time analysis was performed. The results obtained show that the filters respond in a different but not significant way. For the identification of the appropriate mother Wavelet for each scope of analysis, its similarity was considered with the average value of Symlets (sym2 - sym10) and this process was replicated for db Wavelets. Considering the energy of the EEG signals, the db4 filter had a higher presence in 5 electrodes in the Alpha and Delta frequency bands. In the frequency domain, the db5 family has a presence in 12 electrodes in the Beta, Alpha and Delta frequency bands. Regarding time, the sym9 filter has a higher presence in 4 electrodes in the Beta, Theta and Delta frequency bands. The purpose of this work is to provide more information for the proper choice of a mother Wavelet in the EEG signal analysis in asymptomatic volunteers.

Nowadays the World Health Organization (WHO) claims that one billion persons live in the world with some form of disability. From this group, an important number of children with disabilities and communication disorders do not have access to adequate services for special education or health care. In this point, it is important to mention that language and communication are mainstays in the development of several intellectual, social, and cognitive human skills. Therefore, in this paper we present a low-cost robotic assistant that is able to provide support for several activities that must be carried during the speech-language therapy sessions of children with different kinds of disabilities. This assistant is able to register patient's information (personal and clinical data), the results of therapy sessions, and provide remote support to conduct reinforcement activities at patient's home (using mobile devices). With the aim of determining the real feasibility of our system, we have carried out a pilot experiment in 73 therapy sessions with 29 children with disabilities (15 received traditional therapy and 14 received therapy with the assistance of the robot). The results show a rapid adaptation of children to the new tool and good results in phonological, morphosyntactical, and semantic areas.

Each year, trauma accidents generate about five million deaths worldwide. Additionally, these accidents increase the number of people with disabilities in their most productive years. This is a problem that represents social and economic impacts on families. Most of these accidents occur due to a lack of information about prevention. Low-income and middle-income countries are especially affected because here there are no programs to provide information about trauma accident prevention. This document details the development of a robotic assistant to support the process of teaching about accident prevention in different scenarios: prevention of transit accidents (to be hit by a car) as well as prevention of home accidents (burns and intoxications). This assistant is equipped with an audiovisual user-friendly interface for the child and teacher to be used at initial schools. It is focused to teach children from ages three-six years old. The experiment’s results show that the information provided by the robotic assistant during the teaching process is retained by the children over a long time. This methodology provides a more interactive teaching process compared to traditional methods. The design, developing, implementation, experiments, results, and conclusions are detailed below.

In medicine, the diagnosis of diseases by means of image processing has had great acceptability and credibility, which is why it has been able to explore in depth the theme, taking advantage of mainly which is a non-invasive method within the clinical study. Glaucoma is considered as a group of diseases that damages the optic nerve to the point of causing blindness in the eye, with the disadvantage of in most cases do not exhibit early symptoms. Through this work, the processing biomedical images to locate the parameters considered most relevant within images obtained from the back of the eye to make the determination of the existence or not of Glaucoma, a disease that mainly affects physically the dimensions and proportions of the cup and the optical disc. It gets the value of the reason cup/disc and is additional to the characteristics of the rule ISNT, both parameters considered of great importance in the diagnosis of glaucoma. The results obtained serve as a tool to support the specialist in ophthalmology and optometry in conjunction with a presumptive diagnosis of glaucoma, this way it looks for the early detection and prevention of this disease.

Nowadays, more than one billion people live with some form of disability (almost 15% of the world population). In this line, the preterm and low weight in neonates constitute important public health pro blems, especially in low and middle income countries. Infants who survive preterm birth are at increased risk of developing neurological problems such as Cerebral Palsy (CP) and impaired cognitive function. T he CP comprises a series of disturbances in brain functions, motor, hearing, vision and learning for people who have it. In the same way, this disease does not allow to patients to use ICT tools to express their feelings, ideas, and needs, or in general, improve their learning process. On this basis, in this paper we present a ICT-based tool that relies on an embedded system and a mobile application, able to be used by patients that can perform slight head movements, or hand movements. Likewise, our system can detect these movements to help during the co mmunication or learning tasks. The system was evaluated and adjusted in several tests carried out with 47 children (20 girls, and 27 boys). T he achieved results are encouraging, and have shown 89% of acceptance by the patients.

According to World Health Organization (WHO), currently in the World there are around 39 million persons visually impaired. These persons need developing several daily activities in the same way that they are done by people without visual impairments. These activities can be very difficult or dangerous for visually impaired persons (e.g. using public transport, crossing streets, walking alone, etc.). On those grounds, in this paper we present a low-cost system able to meet some needs of blind persons, such as face recognition, reminders to take medications, automatic mail reading, automatic detection of objects and their distance, among others. The proposed system can be easily added to any type of glove. With the aim of determining the real feasibility of the proposed approach, we have carried a pilot experiment with 5 persons with visual impairments. The first part of pilot experiment consisted of determining the response of the device under 4 environments with different light conditions as well as the object detection. In the second part we have measured the perceptions of participants after using device in real conditions. The achieved results shown good agreement of users with respect to weight and handling of the device (80% of participants are strongly agree); whereas the comfort must be improved (60% of participants are agree).