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.