Biblio

NeuronUnityIntgration2.0 (demo video is avilable at http://tiny.cc/u1lz6y) is a plugin for Unity which provides gesture recognition functionalities through the Perception Neuron motion capture suit. The system offers a recording mode, which guides the user through the collection of a dataset of gestures, and a recognition mode, capable of detecting the recorded actions in real time. Gestures are recognized by training Support Vector Machines directly within our plugin. We demonstrate the effectiveness of our application through an experimental evaluation on a newly collected dataset. Furthermore, external applications can exploit NeuronUnityIntgration2.0's recognition capabilities thanks to a set of exposed API.

This paper begins to describe a new kind of database, one that explores a diverse range of movement in the field of dance through capture of different bodies and different backgrounds - or what we are terming movement vernaculars. We re-purpose Ivan Illich's concept of 'vernacular work' [11] here to refer to those everyday forms of dance and organized movement that are informal, refractory (resistant to formal analysis), yet are socially reproduced and derived from a commons. The project investigates the notion of vernaculars in movement that is intentional and aesthetic through the development of a computational approach that highlights both similarities and differences, thereby revealing the specificities of each individual mover. This paper presents an example of how this movement database is used as a research tool, and how the fruits of that research can be added back to the database, thus adding a novel layer of annotation and further enriching the collection. Future researchers can then benefit from this layer, further refining and building upon these techniques. The creation of a robust, open source, movement lexicon repository will allow for observation, speculation, and contextualization - along with the provision of clean and complex data sets for new forms of creative expression.