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In this paper, a multimodal system for recognizing people in intelligent environments is presented. Users are identified and tracked by detecting and recognizing voices and faces through cameras and microphones spread around the environment. This multimodal approach has been chosen to develop a flexible and cheap though reliable system, implemented through consumer electronics. Voice features are extracted through a short time spectrum analysis, while face features are extracted using the eigenfaces technique. The recognition task is achieved through the use of some Support Vector Machines, one per modality, that learn and classify the features of each person, while bindings between modalities are also learnt through a cross-anchoring learning rule based on the mutual exclusivity selection principle. The system has been developed using NMM, a middleware software capable of splitting the sensors processing in several software nodes, making the system scalable in the number of cameras and microphones.

Brain Computer Interface is a system that offers also a support to the patients with neuromuscular diseases as Amyotrophic Lateral Sclerosis. In this paper are presented some works with the aim to integrate brain computer interfaces and mobile robots. The two aim of this project are: (i) to test an improved BCI experience through the help of a physical robot, so that brain signals are stronger stimulate. (ii) to use a remote robot controlled by a highly paralyzed patient via a BCI through a friendly Graphic User. Some preliminary experiments are presented in this paper about one of the possible application: a robotic museum guide (PeopleBot and Pioneer3 robot), that can transmit remote visual perceptions to the patient

In this paper we propose an emotional humanoid robot based on Latent Semantic Analysis, that exhibits an emotional behaviour in the interaction with human. Latent Semantic Analysis (LSA) paradigm is capable to encode the semantics of words using a statistical computation of a large corpus of text. We illustrate how the creation and the use of this emotional conceptual space allows the building of “Latent Semantic Behaviour” because it simulates the emotional associative capabilities of human beings. The presented approach integrates traditional knowledge representation and intuitive capabilities provided by geometric and sub-symbolic information modelling. To validate the effectiveness of the approach we implemented the system using the humanoid robot from Aldebaran, Nao.