De-SIGN: Robust Gesture Recognition in Conceptual Design, Sensor Analysis and Synthesis

Abstract

AbstractDesigning 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.KeywordsSparse RepresentationGesture RecognitionHead Mount DisplayRestrict Isometry PropertyData GloveThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.