Accelerometer-based hand gesture recognition using feature weighted naïve bayesian classifiers and dynamic time warping

Authors:
David Mace;Wei Gao;Ayse Coskun
Affiliations:
Archbishop Mitty High School, San Jose, CA, USA;Boston University, Boston, MA, USA;Boston University, Boston, MA, USA
Venue:
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Year:
2013

Citing 3
Cited 0

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Abstract

Accelerometer-based gesture recognition is a major area of interest in human-computer interaction. In this paper, we compare two approaches: naïve Bayesian classification with feature separability weighting [1] and dynamic time warping [2]. Algorithms based on these two approaches are introduced and the results are compared. We evaluate both algorithms with four gesture types and five samples from five different people. The gesture identification accuracy for Bayesian classification and dynamic time warping are 97% and 95%, respectively.