A Training Gesture-Based-Scroll Visual Artificial Intelligence And Measuring Its Effectiveness Using Hidden-Markov Modeling Methods

Authors

  • Arif Wibisono University of the Indonesian Teachers Association, Indonesia
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DOI:

https://doi.org/10.33557/journalisi.v2i1.58

Keywords:

gesture-based-scroll, visual artificial intelligence, hidden-markov modeling

Abstract

In this article I discuss the method of hand gesture recognition as a visual motion detection based on artificial intelligence by training three main movements namely, scrolling up, scrolling down and stopping based on capturing the front camera image capture speed of 3 fps and measuring its efficiency against the control movements that performed using Hidden-Markov Modeling (HMM) with each catch object scroll up 3 fps / 15 frames scroll down scroll down 3 fps / 15 frames and stop 3 fps / 9 frames, the result is that the most effective hand gesture object training movement is stop gesture with 3 fps / 9 frames because the object's movement is able to be recognized by the system only in the 3rd second image capture frame.

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Published

2020-03-11

Issue

Vol. 2 No. 1 (2020): Journal of Information Systems and Informatics

Section

Articles

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  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
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How to Cite

[1]
A. Wibisono, “A Training Gesture-Based-Scroll Visual Artificial Intelligence And Measuring Its Effectiveness Using Hidden-Markov Modeling Methods”, journalisi, vol. 2, no. 1, pp. 163–168, Mar. 2020, doi: 10.33557/journalisi.v2i1.58.