Gestural Data for Expressive Control: A study in repetition and recognition
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2014-08-28
Language
en
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Abstract
This thesis presents the explorative research into gesture recognition
on unsegmented three-dimensional accelerometer data. The application
context is interactive dance and music performance. Repetition of gesture
is used to distinguish between gesture and non-gesture. Repetition
is detected using an algorithm for pitch detection which is adapted for
multi-dimensional time-series called YIN-MD. Three template based gesture
recognition algorithms are compared on accuracy performance in
different contexts and how they relate to this specific project. Parameter
optimization of the YIN-MD algorithm is performed and pre- and post
processing methods are applied to optimize the detection accuracy for this
project. From the three algorithms GVF, DTW and DTW-PS, the last one
is evaluated as the most promising for this project due to high accuracy
performance and phase invariance.
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Faculteit der Sociale Wetenschappen