This chapter seeks to explore the development of an ambient intelligent dance space. In particular, the chapter proposes intelligent systems that use non-linear optimization as well as symbolic rule-based systems to adjust sound/music, on-stage lighting, and projected visual imagery in terms of movement and color to dynamically reflecting the dancer's arousal state measured through physiological sensors worn by the dancers. To aesthetically adjust physical lighting around the dancers, dancer's location will need to be identified. This variable will be identified using pressure sensor mats installed on the floor to track dancers' movements. Data from these sensors will be passed into a three layered architecture. Layer 1 is composed of a sensor analysis system that analyzes and synthesizes physiological and pressure sensor signals. Layer 2 is composed of intelligent systems that adapt lighting, sound/ music, and visual imagery in terms of pacing and colors to portray the dancer's arousal state. The intelligent physical lighting system dynamically adjusts physical lighting color. The intelligent sound/music system dynamically and unobtrusively adjusts the music/sound in the space. The virtual projected imagery will be created using a cellular automata system that will be intelligently manipulated in terms of cell colors to reflect the arousal state and position of the dancer. Layer 3 translates the high-level adjustments made by the intelligent systems in layer 2 to appropriate lighting board and audio box commands. Using this architecture we seek to extend the mode of dance expression offering a space that performance artists can use as a creative tool that extends the grammar of dance. In this chapter, we will describe this architecture in detail as well as the equipment, control systems, and Artificial Intelligence algorithms used. © 2007 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
El-Nasr, M. S., & Vasilakos, T. (2007). DigitalBeing: An ambient intelligence interactive dance experience. Studies in Computational Intelligence, 72, 233–263. https://doi.org/10.1007/978-3-540-73177-1_8
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