The research aims to develop novel techniques able to recognise different sequential gestures, to the level where they will describe and compute articulated movements in real time. In the context of live media arts, the research outcomes would change the paradigm of creating, learning, performing, designing for live media arts, by giving feedback on performance after analysing, in real time, the streaming video of the performance. Understanding how we recognise and respond to rhythm patterns

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... aesthetically, may support the development of movement description and gesture recognition in real time [5] . One of the main problems in computer vision is defining and describing movement [6] . Traditional approaches mainly rely either on different body-mounted sensors or marker-oriented approaches [2] . Here, the performers have to adapt their natural performance or require substantial training to adopt the tools in their performance. Our principal focus is the development of a system that will free performers from constraints of wearable technology, and allow them to perform in a natural way, without movement and coordination restrictions. Secondly, this will also allow greater precision in analysing the movements while tracking the performance, thus obtaining higher levels of details of movement, allowing in turn the performers to track their performance with greater ease. Key problems in computer vision for the live media arts include the lack of realtime feedback on performance, the inability to seamlessly control body movement in response to audio and visual output, and the unavailability of consistent feedback and evaluation to the performers on their practice in real time. The main goals of our work are: