A gestural media framework : tools for expressive gesture recognition and mapping in rehearsal and performance

Abstract

As human movement is an incredibly rich mode of communication and expression, performance artists working with digital media often use performers' movement and gestures to control and shape that digital media as part of a theatrical, choreographic, or musical performance. In my own work, I have found that strong, semantically-meaningful mappings between gesture and sound or visuals are necessary to create compelling performance interactions. However, the existing systems for developing mappings between incoming data streams and output media have extremely low-level concepts of "gesture." The actual programming process focuses on low-level sensor data, such as the voltage values of a particular sensor, which limits the user in his or her thinking process, requires users to have significant programming experience, and loses the expressive, meaningful, and metaphor-rich content of the movement. To remedy these difficulties, I have created a new framework and development environment for gestural control of media in rehearsal and performance, allowing users to create clear and intuitive mappings in a simple and flexible manner by using high-level descriptions of gestures and of gestural qualities. This approach, the Gestural Media Framework, recognizes continuous gesture and translates Laban Effort Notation into the realm of technological gesture analysis, allowing for the abstraction and encapsulation of sensor data into movement descriptions. As part of the evaluation of this system, I choreographed four performance pieces that use this system throughout the performance and rehearsal process to map dancers' movements to manipulation of sound and visual elements. This work has been supported by the MIT Media Laboratory.