American sign language recognition using leap motion sensor

Ching-Hua Chuan, Eric Regina, Caroline Guardino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

100 Scopus citations

Abstract

In this paper, we present an American Sign Language recognition system using a compact and affordable 3D motion sensor. The palm-sized Leap Motion sensor provides a much more portable and economical solution than Cyblerglove or Microsoft kinect used in existing studies. We apply k-nearest neighbor and support vector machine to classify the 26 letters of the English alphabet in American Sign Language using the derived features from the sensory data. The experiment result shows that the highest average classification rate of 72.78% and 79.83% was achieved by k-nearest neighbor and support vector machine respectively. We also provide detailed discussions on the parameter setting in machine learning methods and accuracy of specific alphabet letters in this paper.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
EditorsCesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages541-544
Number of pages4
ISBN (Electronic)9781479974153
DOIs
StatePublished - Feb 5 2014
Externally publishedYes
Event2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States
Duration: Dec 3 2014Dec 6 2014

Other

Other2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
CountryUnited States
CityDetroit
Period12/3/1412/6/14

Keywords

  • American Sign Language; 3D Leap Motion sensor; k-nearest neighbor; support vector machine; deaf education

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Chuan, C-H., Regina, E., & Guardino, C. (2014). American sign language recognition using leap motion sensor. In C. Ferri, G. Qu, X. Chen, M. A. Wani, P. Angelov, & J-H. Lai (Eds.), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 (pp. 541-544). [7033173] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2014.110