Capturing and Recognizing Expressive Performance Gesture
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(Short paper)
Keywords: Machine Learning, Expressive Performance Gesture, Expressive Movement Recognition.
A better understanding and control of expressive performance gesture potentially could have a large and disruptive impact on electronic media and movement performance practice. We use digitally captured positional data, features extracted from this positional data, and a variety of machine-learning algorithms, to improve the accuracy of recognizing expressive qualities of performance gestures, using concepts derived from Laban Movement Analysis (LMA). Through these methods, we seek to develop better human-computer interfaces, to expand expressive movement vocabularies, and to shift movement aesthetics, by empowering users to exploit their full performance capabilities.
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Full text (PDF) p. 370-373
