Intangible Cultural Heritage (ICH) is a relatively recent term coined to represent living cultural expressions and practices, which are recognised by communities as distinct aspects of identity. The safeguarding of ICH has become a topic of international concern primarily through the work of UNESCO (United Nations Educational, Scientific and Cultural Organisation). However, little research has been done on the role of new technologies in the preservation and transmission of intangible heritage. The chapter examines resources, projects and technologies providing access to ICH and identifies gaps and constraints. It draws on research conducted within the scope of the collaborative research project, i-Treasures. In so doing, it covers the state of the art in technologies that could be employed for access, capture and analysis of ICH in order to highlight how specific new technologies can contribute to the transmission and safeguarding of ICH.

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Human-Robot Collaboration in industrial context requires a smooth, natural and efficient coordination between robot and human operators. The approach we propose to achieve this goal is to use online recognition of technical gestures. In this paper, we present together, and analyze, parameterize and evaluate much more thoroughly, three findings previously unveiled separately by us in several conference presentations: 1/ we show on a real prototype that multi-users continuous real-time recognition of technical gestures on an assembly-line is feasible (≈ 90% recall and precision in our case-study), using only non-intrusive sensors (depth-camera with a top-view, plus inertial sensors placed on tools); 2/ we formulate an end-to-end methodology for designing and developing such a system; 3/ we propose a method for adapting to new users our gesture recognition. Furthermore we present here two new findings: 1/ by comparing recognition performances using several sets of features, we highlight the importance of choosing features that focus on the effective part of gestures, i.e. usually hands movements; 2/ we obtain new results suggesting that enriching a multiusers training set can lead to higher precision than using a separate training dataset for each operator.