De-SIGN

Robust Gesture Recognition in Conceptual Design, Sensor Analysis and Synthesis

Manolya Kavakli, Ali Boyali

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

Designing in Virtual Reality systems may bring significant advantages for the preliminary exploration of the design concept in 3D. In this chapter, our purpose is to provide a design platform in VR, integrating data gloves and the sensor jacket that consists of piezo-resistive sensor threads in a sensor network. Unlike the common gesture recognition approaches, that require the assistance of expensive devices such as cameras or Precision Position Tracker (PPT) devices, our sensor network eliminates both the need for additional devices and the limitation of mobility. We developed a Gesture Recognition System (De-SIGN) in various iterations. De-SIGN decodes design gestures. In this chapter, we present the system architecture for De-SIGN, its sensor analysis and synthesis method (SenSe) and the Sparse Representation-based Classification (SRC) algorithm we have developed for gesture signals, and discussed the system's performance providing the recognition rates. The gesture recognition algorithm presented here is highly accurate regardless of the signal acquisition method used and gives excellent results even for high dimensional signals and large gesture dictionaries. Our findings state that gestures can be recognized with over 99% accuracy rate using the Sparse Representation-based Classification (SRC) algorithm for user-independent gesture dictionaries and 100% for user-dependent.

Original languageEnglish
Title of host publicationAdvances in Robotics and Virtual Reality
EditorsTauseef Gulrez, Aboul Ella Hassanien
Place of PublicationBerlin; New York
PublisherSpringer, Springer Nature
Pages201-225
Number of pages25
Volume26
ISBN (Electronic)9783642233630
ISBN (Print)9783642233623
DOIs
Publication statusPublished - 2012

Publication series

NameIntelligent Systems Reference Library
Volume26
ISSN (Print)18684394
ISSN (Electronic)18684408

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