Understanding the Role of Interactive Machine Learning in Movement Interaction Design

Abstract

Interaction based on human movement has the potential to become an important new paradigm of human-computer interaction. However, high quality, mainstream movement interaction requires effective tools and techniques to support designers. A promising approach to movement interaction design is Interactive Machine Learning, in which designing is done by physically performing an action. This article brings together many different perspectives on understanding human movement knowledge and movement interaction. This understanding shows that the embodied knowledge involved in movement interaction is very different from the representational knowledge involved in a traditional interface, so a very different approach to design is needed. We apply this knowledge to understand why interactive machine learning is an effective tool for motion interaction designers and to make a number of suggestions for future development of the technique.