Abstract
Rheumatic Heart Disease is the serious consequence of repeated episodes of Acute Rheumatic Fever, an autoimmune reaction of a group A streptococcal infection. It's the major cause of heart valve damage, responsible for thousands of deaths per year. The key evidences are changes in the thickness, shape and mobility of the mitral valve leaflets. In this work, an automatic approach is proposed for pathology classification based on motion patterns anterior mitral leaflet, as observed in an echocardiographic video, is proposed. The motion points are extracted from the morphological skeleton of the anterior mitral leaflet and motion velocity and directionality is observed. This data is processed with the Discrete Fourier Transform, and used by several types of classifiers to perform the pathology classification. A Random Forest classifier utilizing the processed data archived a 71% precision and negative predicated value of 58%.

Abstract
In this article, we present a solution for training the mouse input device handling, in order to improve the users' digital skills. Knowing that the widespread access to the computer and the Internet is still made by the use of traditional devices, such as, the keyboard and mouse device (and these devices does not provide natural interaction between people and technology), it is necessary to train for the correct handling. Specifically, mouse usage requires movement control, hand posture and finger positioning, as well as, precision clicks for different functions (one click, two clicks, drag and click) This training is often done in real interaction situations, that could led to inefficiency of use the digital environments and also user's frustration and discouragement. Thus, using simple and intuitive interfaces and providing a set of activities, we allow users to learn and train the motor coordination and basic movements with the mouse. The main results of the study showed that users had a pleasant experience of interaction, considered the interfaces intuitive and affirmed their availability to use it as a training tool.

Abstract

Abstract
Production scheduling is an optimizing problem that can contribute strongly to the competitive capacity of companies producing goods and services. A way to promote the survival and the sustainability of the organizations in this upcoming era of Industry 4.0 (I4.0) is the efficient use of the resources. A complete failure to stage tasks properly can easily lead to a waste of time and resources, which could result in a low level of productivity and high monetary losses. In view of the above, it is essential to analyse and continuously develop new models of production scheduling. This paper intends to present an I4.0 oriented decision support tool to the dynamic scheduling. After a fist solution has been generated, the developed prototype has the ability to create new solutions as tasks leave the system and new ones arrive, in order to minimize a certain measure of performance. Using a single machine environment, the proposed prototype was validated in an in-depth computational study through several instances of dynamic problems with stochastic characteristics. Moreover, a more robust analysis was done, which demonstrated that there is statistical evidence that the proposed prototype performance is better than single method of scheduling and proved the effectiveness of the prototype.

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a rare and disabling neurological disorder caused by, a mutation of the transthyretin gene. One of the disease's characteristics that mostly affects patients' quality of life is its influence on locomotion, with a variable evolution timing. Quantitative motion analysis is useful for assessing motor function, including gait, in diseases affecting movement. However, it is still an evolving field, especially in TTR-FAP, with only a few available studies. A single markerless RGB-D camera pros ides 3-D body joint data in a less expensive, more portable and less intrusive way than reference multi-camera marker-based systems for motion capture. In this contribution, we investigate if a gait analysis system based on a RGB-D camera can be used to detect gait changes over time for a given TTR-FAP patient. 3-D data provided by that system and a reference system were acquired from six TTR-FAP patients, while performing a simple gait task, once and then a year and a half later. For each gait cycle and system, several gait parameters were computed. For each patient, we investigated if the RBG-D camera system is able to detect the existence or not of statistically significant differences between the two different acquisitions (separated by 1.5 years of disease evolution), in a similar way to the reference system. The obtained results show the potential of using a single RGB-D camera to detect relevant changes in spatiotemporal gait parameters (e.g., stride duration and stride length), during TTR-FAP patient follow-up.

Abstract
The concept of health indicates physical, mental and social well-being. Psychological stress is commonly present among freshmen due to social and environmental changes. An approach to study the impact of stress on students relied on biological data assessment. In this work, electrocardiogram signals from first year students, from the Biomedical Engineering course, were collected during an oral presentation, acquiring the RR time series. Linear and nonlinear methodologies are used to extract features that best characterize the RR time series in young subjects under stress conditions.