Support Vector Machine Learning for Gesture Signal Estimation with a Piezo Resistive Fabric Touch Surface

Publication Type  Conference Paper
Year of Publication  2010
Authors  Schmeder, Andrew; Freed, Adrian
Conference Name  NIME
Conference Location  Sydney, Australia
Abstract  The design of an unusually simple fabric-based touch and pressure sensor is introduced. An analysis of the raw sensor data is shown to have significant non-linearities and non-uniform noise. Using support vector machine learning and a state-dependent adaptive filter it is demonstrated that these problems can be overcome. The method is evaluated quantitatively using a statistical estimate of the instantaneous rate of information transfer. The SVM regression alone is shown to improve the gesture signal information rate by up to 20% with zero added latency, and in combination with filtering by 40% subject to a constant latency bound of 10 milliseconds.
Notes  Draft paper in submission. Do not redistribute without permission.
Citation Key  aws-svm-prtouch
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