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A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve "source adaptivity" by directly estimating from the data the "true" score functions of the sources. We describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982). We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function.

In the robotics community, a great number of assistive robots for elderly and handicapped people have been developed in the past few decades. However, very few of them became commercially available. It is often claimed that the major problems for the commercialization of robotic technologies are the "cost" and the "safety." However we believe that the mismatch of "needs in daily lives" and "seeds in the technologies" is also a major problem. In this paper, we describe our novel ideas on the development of assistive robots which fit the real needs of users based on ICF (International Classification of Functioning, Disability and Health), which is a part of the WHO Family of International Classifications for describing whole activities of a person in daily lives. By utilizing ICF, the development process of assistive robots - analyzing and discovering needs in daily lives, designing robots and evaluating the products - will be achieved in an objective manner.

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