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Indoor navigation technologies rely on accurately determining the position of a moving person or robot to guide her actively through the building. This cannot be done using GPS because it is indoors. We have been dealing with this problem in the context of designing and building a system for helping blind people navigate in public buildings. However, indoor localization based on RSSI is not very accurate, since the quality of the signal is influenced by many factors that include, for instance, wall reflections or people passing by. The fact that the person is moving also introduces several degrees of uncertainty, including a changing number and placement of beacons in-range. The challenge is therefore to develop mechanisms to handle uncertainty. In this paper we propose and evaluate uncertainty-handling mechanisms. [ABSTRACT FROM AUTHOR]

Nowadays, Human-Computer Interaction (HCI) systems e.g. keyboard, mouse and touch screen are frequently used by anyone. However, those ways to interact with computers may be not suitable for disabled persons. Brain-Computer Interface (BCI) is an HCI system which can be used as an alternative for these persons. This approach allows operating a computer using only the brain signals, for instance, imagine a situation where a participant only needs to think about an action in order to make it happen: to move, to select an object, to shift gaze, to control the movements of their (virtual) body, or to design the environment itself, by “thought” alone. In this paper we propose a BCI framework to process brain signals resulting from imagination processes that is used together with serious games to teach or improve autonomy of physically handicapped people. Two simple scenarios based on grasp and eye-gaze imagination games were developed to test the framework. Games consist on choosing the right object to put on the recycling bin or to choose a piece of a puzzle to fit the other one in a white board. Through the proposed approach we are able to discriminate the direction intended by the player. Our results show that we achieve about 80% of accuracy when a block of 30 seconds of imagination is considered. [ABSTRACT FROM AUTHOR]