Abstract
Accessibility and inclusiveness of people with disabilities is a recurring theme that is already perceived as an issue in the field of human rights. Ramps, elevators, among other devices aim at the inclusion of these individuals with limited mobility. Various types of motor limitations, specially partial limitations, are linked to corresponding physical-motor rehabilitation process, with the purpose of reducing or eliminating the patient's dependence on a caregiver or devices for adaptation. Patients with motor disabilities must practice physiotherapeutical exercises along a physician in order to perform body and muscle analysis to ensure the patient's well-being. To reach a more accurate analysis, physiotherapists use a range of devices to acquire patient data, such as the spirometer, to acquire the patient's breath intensity and lung capacity. Similarly, there are other technologies capable of acquiring motion data and quantifying them. This work aims to develop a system that, paired together with an exercise game project (exergame), can acquire and transmit the motion data acquired in-game for an easier and faster analysis of the patient's growth, relying on graphs, tables, and other visual indicators to improve the evaluation of physiotherapeutic treatments. The usage together with an exergame also has benefits such as increased patient compliance with the treatment and improvements in well-being.

Abstract
Chikungunya virus disease transmitted by the sting of the mosquito 'Aedes aegypti' presenting an epidemic in some regions. In order to have an early diagnosis and the best treatment technique, it establishes the study of inhibitors for laboratory elaboration of a drug from molecular docking. As a result you have a better chance of using Suramin followed by Silibin.

Abstract
This article describes a classification model of optical coherence tomography images using convolution neural network. The dataset used was the Labeled Optical Coherence Tomography provided by (Kermany et al., 2018) with a total of 84495 images, with 4 classes: normal, drusen, diabetic macular edema and choroidal neovascularization. To evaluate the generalization capacity of the models k-fold cross-validation was used. The classification models were shown to be efficient, and as a result an average accuracy of 94.35% was obtained.

Abstract
Noting recent advances in the field of image classification, where convolutional neural networks (CNNs) are used to classify images with high precision. This paper proposes a method of classifying breathing sounds using CNN, where it is trained and tested. To do this, a visual representation of each audio sample was made that allows identifying resources for classification, using the same techniques used to classify images with high precision.For this we used the technique known as Mel Frequency Cepstral Coefficients (MFCCs). For each audio file in the dataset, we extracted resources with MFCC which means we have an image representation for each audio sample. The method proposed in this article obtained results above 74%, in the classification of respiratory sounds used in the four classes available in the database used (Normal, crackles, wheezes, Both).

Abstract
This article reports a pedagogical experience developed within the scope of a Ph.D. program in Electrical and Computer Engineering with application to Education. Starting with a contextualization on the evolution of computers and Computational Thinking, the article describes the platform used in this study - code.org -, highlighting the strengths that captivate the students. In the Case Study topic, we describe the study carried out, starting with a description of the students involved, followed by a description of the process and the analysis of the results, ending with the evaluation process performed by the students. The article ends concluding that code.org is a valid option to develop computational thinking at early-ages. Copyright

Abstract
The increasing interest in connecting small sensors to the internet has led to the development of operating systems able to support all network, graphical and server functionalities over diverse embedded hardware. Globaltronic, a company based in Portugal, has developed an embedded computer called WiiPiiDo, powered by an ARM SoC (System on a Chip), which is highly specialized for IoT applications. It features NB-IoT - LTE Cat NB1 (Narrow Band IoT) to ensure robust connection to the Internet even in harsh conditions, and allows rapid development of complete IoT solutions for end-users. The development of a Linux image that exposes all the potential of the hardware platform is a must and will provide extra value to it. To create such an operating system, we examined the Yocto Project, which is a Linux building environment that is gaining a growing community of users, particularly enterprises. Nevertheless, Yocto is not the only choice for the embedded platform developer community. In fact, Armbian, a Debian/Ubuntu based distribution, appears as an popular alternative for embedded development in ARM boards. In this paper we show the steps we took from testing the first boot on the WiiPiiDo board until the development of the supporting operating system, finalizing with performance tests. We present a comparison of the two build systems that were used and report the results from the performance tests. © 2020, Springer Nature Switzerland AG.