Device agnostic 3D gesture recognition using hidden Markov models

Authors:
Anthony Whitehead;Kaitlyn Fox
Affiliations:
Carleton University, Ottawa, Ontario, Canada;Carleton University, Ottawa, Ontario, Canada
Venue:
Future Play '09 Proceedings of the 2009 Conference on Future Play on @ GDC Canada
Year:
2009

Citing 3
Cited 0

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Abstract

Hidden Markov Models have been effectively used in pattern recognition systems in the past. In this work, we identify the necessary elements to successfully use an HMM system for 3D gesture recognition regardless of the sensor device being used. So long as the sensor system itself is capable of outputting information about the 3 axes of motion (X, Y, and Z), that information can be used in this generic model for accurate, high speed gesture recognition. The proposed system works with accelerometer data, positional data and gyro data alike.