Capturing and Recognizing Expressive Performance Gesture

Symposium:

• ISEA2015: 21st International Symposium on Electronic Art
• More presentations from ISEA2015:  

• Movement and Bodies

• Capturing and Recognizing Expressive Performance Gesture

• Michael Junokas
• Kyungho Lee
• Mohammad Amanzadeh
• Guy Garnett

• (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.

• Full text (PDF)  p. 370-373

Category:

• Paper