Interactive Machine Learning for Embodied Interaction Design: A tool and methodology

Abstract

As immersive technologies are increasingly being adopted by artists, dancers and developers in their creative work, there is a demand for tools and methods to design compelling ways of embodied interaction within virtual environments. Interactive Machine Learning allows creators to quickly and easily implement movement interaction in their applications by performing examples of movement to train a machine learning model. A key aspect of this training is providing appropriate movement data features for a machine learning model to accurately characterise the movement then recognise it from incoming data. We explore methodologies that aim to support creators' understanding of movement feature data in relation to machine learning models and ask how these models hold the potential to inform creators' understanding of their own movement. We propose a 5-day hackathon, bringing together artists, dancers and designers, to explore designing movement interaction and create prototypes using new interactive machine learning tool InteractML.