Hand motion tracking based on gesture understanding using leap gesture for virtual 3D batik gallery

Virtual batik 3D gallery is designed as one of the efforts utilizations of digital-based on media for Industrial Revolution 4.0 where the flow of globalization is becoming sophisticated which emphasizes the digital economical pattern, artificial intelligence, big data, robotic. This research emphasi...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-02, Vol.1469 (1), p.12099
Main Authors: Padmasari, A C, Hariadi, M, Christyowidiasmoro, C
Format: Article
Language:English
Subjects:
Artificial intelligence
Globalization
Keyboards
Modelling
Mouse devices
Physics
Three dimensional models
Three dimensional motion
Tracking control
Virtual reality
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Summary:Virtual batik 3D gallery is designed as one of the efforts utilizations of digital-based on media for Industrial Revolution 4.0 where the flow of globalization is becoming sophisticated which emphasizes the digital economical pattern, artificial intelligence, big data, robotic. This research emphasizes on hand motion tracking based scientific knowledge on Gesture Understanding using Leap gesture for the 3-Dimensional Virtual Batik Gallery. Gesture-understanding based on Hand Motion Tracking which advocates Leap Gesture for a 3D Virtual Batik Gallery functions as an interactive virtual media replacing part of the functions Keyboard and Mouse process. This is aimed at varying the media packaging, especially for information providers in terms of how they can relay the information quickly to others, designing application, or documenting data. The method used in this research is Design and Development (D&D) and required three steps involving the process of modelling the initial design (3D modelling) using Normal Map technique, the process of making gesture scenario in the form of FSM in every room, and gesture design using hand motion tracking in Leap Motion controller. The result of the study was in the forms of responses to hand motion tracking in a 3D batik gallery, which was grouped into 3 hand-gesture-understanding concepts: Tap Gesture with motion response description for GPU (16 mm/s 60 FPS), (10 mm / s 100 FPS), (5 ms/200 FPS), CPU (33 ms 30FPS), and (16 ms / 60 FPS); for describing and detecting Flyhand Gesture on GPU (16 mm/s) (60 FPS10 mm / s) (100 FPS5 m/s 200 FPS) on CPU (33 ms/30FPS) (16 ms / 60 FPS); for Trusther in (GPU = 16 mm/s) (60 FPS33mm / s) (100 FPS 16 ms / 60 FPS) CPU (33 ms 30 FPS 10 ms) (60 FPS 33 ms 5 ms 200 FPS); and for Hold Hand Gesture on CPU (33 ms 30 FPS) (66 ms 60 FPS) and the response on GPU (33 ms 30 FPS) (66 ms 60 FPS).
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1469/1/012099