Mobile gesture recognition using hierarchical recurrent neural network with Bidirectional Long Short-Term Memory

Myeong Chun Lee, Sung Bae Cho

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

3 Citations (Scopus)

Abstract

As the sensors embedded to a smartphone are proliferating, many application systems for context-aware services are actively investigated. This paper proposes a gesture recognition system with smartphones for better interface. It is important to maintain high accuracy even with the large number of gestures. To improve the accuracy, we adopt the recurrent neural network based on hierarchical BLSTM (Bidirectional Long Short-Term Memory). The first level BLSTMs are used to discriminate the gestures and nongestures, and the second level BLSTMs classify the input into one of twenty gestures. Experiments with 24,850 sequence data consisting of 11,885 gesture sequences and 12,965 non-gesture sequences confirm the high performance of the proposed method over the competitive alternatives.

Original languageEnglish
Title of host publicationUBICOMM 2012 - 6th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
Pages138-141
Number of pages4
Publication statusPublished - 2012
Event6th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2012 - Barcelona, Spain
Duration: 2012 Sep 232012 Sep 28

Publication series

NameUBICOMM 2012 - 6th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies

Other

Other6th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2012
Country/TerritorySpain
CityBarcelona
Period12/9/2312/9/28

All Science Journal Classification (ASJC) codes

  • Software

Fingerprint

Dive into the research topics of 'Mobile gesture recognition using hierarchical recurrent neural network with Bidirectional Long Short-Term Memory'. Together they form a unique fingerprint.

Cite this