A recurrent neural network with non-gesture rejection model for recognizing gestures with smartphone sensors

Myeong Chun Lee, Sung Bae Cho

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

Abstract

Gesture recognition provides a new interface to user. Various methods for the gesture recognition are feasible in smartphone environment since a number of sensors attached are gradually increasing. In this paper, we propose a gesture recognition method using smartphone accelerometer sensors. The high false-positive rate is definite if the gesture sequence data are increased. We have modified BLSTM (Bidirectional Long Short-Term Memory) recurrent neural network with non-gesture rejection model to deal with the problem. A BLSTM model classifies the input into the gesture and non-gesture classes, and the specific BLSTM models for the gestures further classify it into one of twenty gestures. 24,850 sequence data are used for the experiment, and it consists of 11,885 gesture sequences and 12,965 non-gesture sequences. The proposed method shows higher accuracy than the standard BLSTM.

Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Proceedings
Pages40-46
Number of pages7
DOIs
Publication statusPublished - 2013 Dec 1
Event5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013 - Kolkata, India
Duration: 2013 Dec 102013 Dec 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8251 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013
CountryIndia
CityKolkata
Period13/12/1013/12/14

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lee, M. C., & Cho, S. B. (2013). A recurrent neural network with non-gesture rejection model for recognizing gestures with smartphone sensors. In Pattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Proceedings (pp. 40-46). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8251 LNCS). https://doi.org/10.1007/978-3-642-45062-4_4