Abstract
Taking into consideration a current legal Portuguese framework about accessibility requirements for websites and mobile applications of the public sector, we present an e-Learning project aiming to enhance awareness about the need of the development of accessible Web contents, thus promoting digital inclusion. In this context, in this paper we present a description of a Massive Open Online Courses (MOOC) on Web accessibility entitled: "Inclusive Web - How to develop inclusive web contents?" In this paper, we described the creation of this MOOC, not only the program contents, learning methodologies and evaluation descriptions but also present satisfaction and learning results of this project. This first edition counted on 174 students, among them, 2 with special needs, from 4 different countries, mostly professionals who develop web content, such as: students, professionals and enthusiasts in the areas of Informatics, Multimedia, Information and Communication Technologies (ICT), Digital Media, Web Design, Teaching and Special Education. The feedback results showed that despite there were a few features that must be improved, overall the applicants enjoyed and stated that the course was a good learning resource to start in the Web Accessibility topic.

Abstract
The recovery of walking function following spinal cord injury (SCI) is of major importance to patients and clinicians. In experimental SCI studies, a rat model is widely used to assess walking function, following thoracic spinal cord lesion. In an effort to provide a resource which investigators can refer to when seeking the most appropriate functional assay, the authors have compiled and categorized the behavioral assessments used to measure the deficits and recovery of the gait in thoracic SCI rats. These categories include kinematic and kinetic measurements. Within this categorization, we discuss the advantages and disadvantages of each type of measurement. The present review includes the type of outcome data that they produce, the technical difficulty and the time required to potentially train the animals to perform them, and the need for expensive or highly specialized equipment. The use of multiple kinematic and kinetic parameters is recommended to identify subtle deficits and processes involved in the compensatory mechanisms of walking function after experimental thoracic SCI in rats.

Abstract
This paper presents an innovative and motivating methodology to learn vision systems using a humanoid robot, NAO robot. Vision systems are an area of growing development and interest of engineering students. This approach to learning was applied in students of Master of Electrical Engineering. The goal is to introduce students the main approaches of visual object recognition and human face recognition using computer vision techniques to be embedded in a social robot and therefore he is able to iteract with human beings. NAO robot as an educational platform easy to learn how to program, and it has a high sensory ability and two cameras that can capture the images for processing.

Abstract
Analysis of student concentration can help to enhance the learning process. Emotions are directly related and directly reflect students’ concentration. This task is particularly difficult to implement in an e-learning environment, where the student stands alone in front of a computer. In this paper, a prototype system is proposed to figure out the concentration level in real-time from the expressed facial emotions during a lesson. An experiment was performed to evaluate the prototype system that was implemented using a client-side application that uses the C# code available in Microsoft Azure Emotion API. We have found that the emotions expressed are correlated with the concentration of the students, and devised three distinct levels of concentration (high, medium, and low). © Springer Nature Switzerland AG 2019.

Abstract
If done properly, physical exercise can help maintain fitness and health. The benefits of physical exercise could be increased with real time monitoring by measuring physical exercise intensity, which refers to how hard it is for a person to perform a specific task. This parameter can be estimated using various sensors, including contactless technology. Physical exercise intensity is usually synchronous to heart rate; therefore, if we measure heart rate, we can define a particular level of physical exercise. In this paper, we proposed a Convolutional Neural Network (CNN) to classify physical exercise intensity based on the analysis of facial images extracted from a video collected during sub-maximal exercises in a stationary bicycle, according to standard protocol. The time slots of the video used to extract the frames were determined by heart rate. We tested different CNN models using as input parameters the individual color components and grayscale images. The experiments were carried out separately with various numbers of classes. The ground truth level for each class was defined by the heart rate. The dataset was prepared to classify the physical exercise intensity into two, three, and four classes. For each color model a CNN was trained and tested. The model performance was presented using confusion matrix as metrics for each case. The most significant color channel in terms of accuracy was Green. The average model accuracy was 100%, 99% and 96%, for two, three and four classes classification, respectively. © 2019, Springer Nature Switzerland AG.

Abstract
In order to determine the physiological state of a plant it is necessary to monitor it throughout the developmental period. One of the main parameters to monitor is the Leaf Area Index (LAI). The objective of this work was the development of a non-destructive methodology for the LAI estimation in wine growing. This method is based on stereo images that allow to obtain a bard 3D representation, in order to facilitate the segmentation process, since to perform this process only based on color component becomes practically impossible due to the high complexity of the application environment. In addition, the Normalized Difference Vegetation Index will be used to distinguish the regions of the trunks and leaves. As an low-cost and non-evasive method, it becomes a promising solution for LAI estimation in order to monitor the productivity changes and the impacts of climatic conditions in the vines growth. © Springer Nature Switzerland AG 2019.