Design of a low-false-positive gesture for awearable device

Ryo Kawahata, Atsushi Shimada, Takayoshi Yamashita, Hideaki Uchiyama, Rin Ichiro Taniguchi

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

8 Citations (Scopus)

Abstract

As smartwatches are becoming more widely used in society, gesture recognition, as an important aspect of interaction with smartwatches, is attracting attention. An accelerometer that is incorporated in a device is often used to recognize gestures. However, a gesture is often detected falsely when a similar pattern of action occurs in daily life. In this paper, we present a novel method of designing a new gesture that reduces false detection. We refer to such a gesture as a low-false-positive (LFP) gesture. The proposed method enables a gesture design system to suggest LFP motion gestures automatically. The user of the system can design LFP gestures more easily and quickly than what has been possible in previous work. Our method combines primitive gestures to create an LFP gesture. The combination of primitive gestures is recognized quickly and accurately by a random forest algorithm using our method. We experimentally demonstrate the good recognition performance of our method for a designed gesture with a high recognition rate and without false detection.

Original languageEnglish
Title of host publicationICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages581-588
Number of pages8
ISBN (Electronic)9789897581731
DOIs
Publication statusPublished - 2016
Event5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016 - Rome, Italy
Duration: Feb 24 2016Feb 26 2016

Publication series

NameICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

Other

Other5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016
Country/TerritoryItaly
CityRome
Period2/24/162/26/16

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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