Applied Sciences · 2019 · 110 citations · 37 references
ComputingAmerican Sign Language3D ModelingComputer VisionImmersive VisualizationAction RecognitionGesture AnalysisGesture RecognitionMultimodal InteractionVirtual RealityVirtual WorldsGesture LearningGesture RecognizersGesture-based InterfacesHand InteractionHands IdentificationHidden Markov AnalysisSign Language Gesture AnalysisSign Language Recognition
We perform gesture recognition in a Virtual Reality (VR) environment using data produced by the Leap Motion device. Leap Motion generates a virtual three-dimensional (3D) hand model by recognizing and tracking user‘s hands. From this model, the Leap Motion application programming interface (API) provides hand and finger locations in the 3D space. We present a system that is capable of learning gestures by using the data from the Leap Motion device and the Hidden Markov classification (HMC) algorithm. We have achieved the gesture recognition accuracy (mean ± SD) is 86.1 ± 8.2% and gesture typing speed is 3.09 ± 0.53 words per minute (WPM), when recognizing the gestures of the American Sign Language (ASL).
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