| This thesis aims to explore the language of different bodies in the field of dance by analyzing the habitual patterns of dancers from different backgrounds and vernaculars. Contextually, the term habitual patterns is defined as the postures or poses that tend to re-appear, often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing the movement vocabulary of a dancer to reveal his/her unique fingerprint.;The proposed approach for uncovering these movement patterns is to use a clustering technique; mainly k-means. In addition to a static method of analysis, this paper uses an online method of clustering using a streaming variant of k-means that integrates into the flow of components that can be used in a real-time interactive dance performance. The computational system is trained by the dancer to discover identifying patterns and therefore it enables a feedback loop resulting in a rich exchange between dancer and machine. This can help break a dancer's tendency to create similar postures, explore larger kinespheric space and invent movement beyond their current capabilities.;This paper describes a project that distinguishes itself in that it uses a custom database that is curated for the purpose of highlighting the similarities and differences between various movement forms. It puts particular emphasis on the process of choosing source movement qualitatively, before the technological capture process begins. |
