ABSTRACT

This chapter focuses on the potential of physiological interfaces to capture performer gesture to create embodied interaction with computer music systems. It begins with a brief history of the use of physiological signals in musical performance. The chapter introduces the range of physiological signals, and focus on the electromyogram (EMG), reporting muscle tension. It then presents techniques for using the EMG in music, including signal pre-processing and feature extraction, and analysis by machine learning. The chapter discusses the challenges of reproducibility and situates the EMG in a multi-modal context with other sensing modalities. The challenges EMG poses—the degrees of freedom problem and the lack of direct link between signal and resulting movement—provide the richness in the signal that cannot be gleaned from other, physical sensors. The chapter concludes by proposing gesture "power" as one low-level feature that in part represents Laban's notion of "effort" to demonstrate the potential of the EMG to capture expressive musical gesture.