Abstract
Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value<0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

Abstract
Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Abstract
The development of new technologies, information systems, decision support systems and clinical parameters prediction algorithms using machine learning and data mining, opens a new outlook in many areas of health. In this context, the concept of Quality of Life (QOL) has relevance in health and the possibility of integrate this measure in developing systems Decision Support Clinic (SADC). Through individual expectation of physical well-being, psychological, mental, emotional and spiritual patient, clinical variables and quality of life assessment, we intend to make a study of data to establish correlations with clinical data and pharmaceutical data, socio-economic factors, among others, for obtaining knowledge in terms of behavioral patterns of chronically ill, reaching a number of reliable data and easily accessible, capable of enhancing the decision-making process on the part of specialist medical teams, seeking to improve treatments and consequently the quality of life related to health chronically ill. This paper studied and compared related studies that develop systems for decision support and prediction in the clinical area, with emphasis on studies in the area of quality of life.

Abstract
Soccer Humanoid robots must be able to fulfill their tasks in a highly dynamic soccer field, which requires highly responsive and dynamic locomotion. It is very difficult to keep humanoids balance during walking. The position of the Zero Moment Point (ZMP) is widely used for dynamic stability measurement in biped locomotion. In this paper, we present an omnidirectional walk engine, which mainly consist of a Foot planner, a ZMP and Center of Mass (CoM) generator and an Active balance loop. The Foot planner, based on desire walk speed vector, generates future feet step positions that are then inputs to the ZMP generator. The cart-table model and preview controller are used to generate the CoM reference trajectory from the predefined ZMP trajectory. An active balance method is presented which keeps the robot's trunk upright when faced with environmental disturbances. We have tested the biped locomotion control approach on a simulated NAO robot. Our results are encouraging given that the robot has been able to walk fast and stably in any direction with performances that compare well to the best RoboCup 2012 3D Simulation teams.

Abstract
Nowadays, the increase of robotic technology application to industry scenarios is notorious. Proposals for new effective solutions are in continuous development once industry needs a constantly improvement in time as well as in production quality and efficiency. The EuRoC research project proposes a scientific competition in which research and industry manufacturers joint teams are encouraged to develop and test solutions that can solve several issues as well as be useful in manufacturing improvement. This paper presents the TIMAIRIS architecture and approach used in the Challenge 2 - Stage II - Benchmarking phase, namely regarding the perception, manipulation and planning strategy that was applied to achieve the tasks objectives. The used approach proved to be quite robust and efficient, which allowed us to rank first in the Benchmarking phase. © 2017 IEEE.

Abstract