Seeing, Sensing and Recognizing Laban Movement Qualities

Human movement has historically been approached as a functional component of interaction within human computer interaction. Yet movement is not only functional, it is also highly expressive. In our research, we explore how movement expertise as articulated in Laban Movement Analysis (LMA) can contribute to the design of computational models of movement's expressive qualities as defined in the framework of Laban Efforts. We include experts in LMA in our design process, in order to select a set of suitable multimodal sensors as well as to compute features that closely correlate to the definitions of Efforts in LMA. Evaluation of our model shows that multimodal data combining positional, dynamic and physiological information allows for a better characterization of Laban Efforts. We conclude with implications for design that illustrate how our methodology and our approach to multimodal capture and recognition of Effort qualities can be integrated to design interactive applications.