Robust discrimination method of the electrooculogram signals for human-computer interaction controlling mobile robot

Youngmin Kim, Nakju Doh, Youngil Youm, Wan Kyun Chung

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

In this paper, a robust algorithm that discriminates various eye motions from the ElectroOculoGram OEOG) signals is proposed. Previous researches that use the EOG only focused on saccadic motions or blinks. However, we cover all eye motions including double/triple blinks and left/right winks. Furthermore, we suggest a novel method, which removes noises of the EOG, to increase the robustness of the discrimination. The method is called "an ideal velocity shape algorithm" which compares the real velocity of the EOG with an ideal velocity designed under a noise free assumption. This algorithm significantly reduces the effects of the noises and thus enhances the robustness. Detected eye motions are used for a Human-Computer Interaction (HCI) between a person and a mobile robot. In the HCI, the person successfully controlled the robot for a target tracking and point stabilization.

Original languageEnglish
Pages (from-to)319-336
Number of pages18
JournalIntelligent Automation and Soft Computing
Volume13
Issue number3
Publication statusPublished - 2007 Oct 10

Fingerprint

Human computer interaction
Mobile Robot
Mobile robots
Discrimination
Motion
Interaction
Person
Robustness
Target tracking
Robust Algorithm
Target Tracking
Stabilization
Robots
Robot
Cover
Human

Keywords

  • Discrimination
  • Electrooculogram
  • Eye signal
  • Human-computer interaction
  • Mobile robot

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence

Cite this

Robust discrimination method of the electrooculogram signals for human-computer interaction controlling mobile robot. / Kim, Youngmin; Doh, Nakju; Youm, Youngil; Chung, Wan Kyun.

In: Intelligent Automation and Soft Computing, Vol. 13, No. 3, 10.10.2007, p. 319-336.

Research output: Contribution to journalArticle

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