This article focuses on evaluating the usability of an intelligent wheelchair (IW) in both real and simulated environments. The wheelchair is controlled at a high-level by a flexible multimodal interface, using voice commands, facial expressions, head movements and joystick as its main inputs. A quasi-experimental design was applied including a deterministic sample with a questionnaire that enabled to apply the system usability scale. The subjects were divided in two independent samples: 46 individuals performing the experiment with an IW in a simulated environment (28 using different commands in a sequential way and 18 with the liberty to choose the command); 12 individuals performing the experiment with a real IW The main conclusion achieved by this study is that the usability of the IW in a real environment is higher than in the simulated environment. However, there were not statistical evidences to affirm that there are differences between the real and simulated wheelchairs in terms of safety and control. Also, most of users considered the multimodal way of driving the wheelchair very practical and satisfactory. Thus, it may be concluded that the multimodal interfaces enables very easy and safe control of the IW both in simulated and real environments.

Machine Learning (ML) and Knowledge Discovery (KD) are research areas with several different applications but that share a common objective of acquiring more and new information from data. This paper presents an application of several ML techniques in the identification of the opponent team and also on the classification of robotic soccer formations in the context of RoboCup international robotic soccer competition. RoboCup international project includes several distinct leagues were teams composed by different types of real or simulated robots play soccer games following a set of pre-established rules. The simulated 2D league uses simulated robots encouraging research on artificial intelligence methodologies like high-level coordination and machine learning techniques. The experimental tests performed, using four distinct datasets, enabled us to conclude that the Support Vector Machines (SVM) technique has higher accuracy than the k-Nearest Neighbor, Neural Networks and Kernel Naïve Bayes in terms of adaptation to a new kind of data. Also, the experimental results enable to conclude that using the Principal Component Analysis SVM achieves worse results than using simpler methods that have as primary assumption the distance between samples, like k-NN.