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

Andrew Schmeder, and Adrian Freed

Proceedings of the International Conference on New Interfaces for Musical Expression

Abstract:

The design of an unusually simple fabric-based touchlocation and pressure sensor is introduced. An analysisof the raw sensor data is shown to have significant nonlinearities and non-uniform noise. Using support vectormachine learning and a state-dependent adaptive filter itis demonstrated that these problems can be overcome.The method is evaluated quantitatively using a statisticalestimate of the instantaneous rate of information transfer.The SVM regression alone is shown to improve the gesturesignal information rate by up to 20% with zero addedlatency, and in combination with filtering by 40% subjectto a constant latency bound of 10 milliseconds.

Citation:

Andrew Schmeder, and Adrian Freed. 2010. Support Vector Machine Learning for Gesture Signal Estimation with a Piezo-Resistive Fabric Touch Surface. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.1177893

BibTeX Entry:

  @inproceedings{Schmeder2010,
 abstract = {The design of an unusually simple fabric-based touchlocation and pressure sensor is introduced. An analysisof the raw sensor data is shown to have significant nonlinearities and non-uniform noise. Using support vectormachine learning and a state-dependent adaptive filter itis demonstrated that these problems can be overcome.The method is evaluated quantitatively using a statisticalestimate of the instantaneous rate of information transfer.The SVM regression alone is shown to improve the gesturesignal information rate by up to 20% with zero addedlatency, and in combination with filtering by 40% subjectto a constant latency bound of 10 milliseconds.},
 address = {Sydney, Australia},
 author = {Schmeder, Andrew and Freed, Adrian},
 booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
 doi = {10.5281/zenodo.1177893},
 issn = {2220-4806},
 keywords = {gesture signal processing, support vector machine, touch sensor},
 pages = {244--249},
 title = {Support Vector Machine Learning for Gesture Signal Estimation with a Piezo-Resistive Fabric Touch Surface},
 url = {http://www.nime.org/proceedings/2010/nime2010_244.pdf},
 year = {2010}
}