Interactive Machine Learning for Movement Interaction in VR

Full body movement is a powerful way of interacting with virtual reality experiences. Not only does it reproduce real world interactions, it can also have positive effects on emotions. However, designing effective movement interaction can be hard, as our knowledge about how we move is tacit and embodied, meaning that we can move without knowing exactly how we make those movements. This makes it hard to explicitly program movement interaction. Interactive Machine Learning (IML) is an alternative approach in which movement interaction is designed and implementing by providing examples of movement. This paper presents IntearctML, a movement interaction design platform based on IML, as well as a case study of using it to create a VR experience called Dolittle VR.