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

  • Arif Wibisono University of the Indonesian Teachers Association
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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References

[1] L. Barkhuus, “UbiComp 2002 international conference,” Ubiquitous Comput. Transpar. Context. Mob. Comput., pp. 67–68, 2002.
[2] T. Hoye and J. Kozak, “Tenth Annual Freshman Conference Touch Screens: a Pressing Technology Infrared Touch Screens,” pp. 1–6, 2010.
[3] M. Correa, J. Ruiz-Del-Solar, R. Verschae, J. Lee-Ferng, and N. Castillo, “Real-time hand gesture recognition for human robot interaction,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5949 LNAI, no. June, pp. 46–57, 2010, doi: 10.1007/978-3-642-11876-0_5.
[4] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, “Robust Part-Based Hand Gesture Recognition Using Kinect Sensor,” IEEE Trans. Multimed., vol. 15, no. 5, pp. 1110–1120, 2013, doi: 10.1109/TMM.2013.2246148.
[5] M. Correa, J. Ruiz-Del-Solar, R. Verschae, J. Lee-Ferng, and N. Castillo, “Real-time hand gesture recognition for human robot interaction,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5949 LNAI, no. May 2014, pp. 46–57, 2010, doi: 10.1007/978-3-642-11876-0_5.
[6] V. Sutojo, T; Mulyanto, Edi; Suhartono, “Kecerdasan Buatan,” pp. 211–235, 2011.
[7] S. Russell and P. Norvig, “Artificial Intelligience: a modern approach.” p. 1151, 2016, doi: 10.1017/CBO9781107415324.004.
[8] Id.techinasia.co, “vision-ai-penerapannya-di-industri.” [Online]. Available: https://id.techinasia.com/vision-ai-penerapannya-di-industri. [Accessed: 01-Mar-2020].
[9] E. F. Yuwitaning, B. Hidayat, and N. Andini, “Implementasi Metode Hidden Markov Model untuk Deteksi Tulisan Tangan,” e-Proceeding Eng., vol. 1, no. 1, pp. 396–402, 2014.
[10] A. han, Muhammad & Malik, Rabbiya & Siddique, Ayesha & Nawaz, “citation-332392507.” Elsevier B.V., Netherlands, doi: https://doi.org/10.1016/j.ijleo.2019.04.068.
[11] B. H. Juang and L. R. Rabiner, “Hidden Markov Models for Speech Recognition,” Technometrics, vol. 33, no. 3, pp. 251–272, Aug. 1991, doi: 10.1080/00401706.1991.10484833.
[12] A. Wijaya, “APLIKASI HIDDEN MARKOV MODEL PADA PINTU GESER BERBASIS SUARA.” Perpustakaan Universitas Snata Darma Yogyakarta, Yogyakarta, 2011.
[13] A. K. Nisa’, “Aplikasi Metode Hidden Markov Model Untuk Identifikasi Wajah Individu,” p. 124, 2017.
[14] J. L. H. F. Hochheiser, Research Methods in Human-Computer Interaction. 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States: Morgan Kaufmann Publisher An Imprint Of Elsevier.
Published
2020-03-11
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How to Cite
Wibisono, A. (2020). A Training Gesture-Based-Scroll Visual Artificial Intelligence And Measuring Its Effectiveness Using Hidden-Markov Modeling Methods. Journal of Information Systems and Informatics, 2(1), 163-168. https://doi.org/10.33557/journalisi.v2i1.58