Naïve Bayes Classifier for Hand Gestures Recognition

Imanuel Simatupang, Daniel Sutopo Pamungkas, Sumantri K. Risandriya

Abstract

This paper provides recognizing the five gestures of the fingers using Naïve Bayes method. The electromyography signal (EMG) is utilized to recognize the fingers movement. A myo armband is used to obtain the signal. The average success rate of the system is about 90.61%. To verify the results, the outputs of the system are used to control a mobile robot. The results show that the system is able to control the movement of the robot.

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Paper Citation


in Harvard Style

Simatupang I., Pamungkas D. and Risandriya S. (2020). Naïve Bayes Classifier for Hand Gestures Recognition. In Proceedings of the 3rd International Conference on Applied Engineering - Volume 1: ICAE, ISBN 978-989-758-520-3, pages 110-114. DOI: 10.5220/0010352601100114


in Bibtex Style

@conference{icae20,
author={Imanuel Simatupang and Daniel Sutopo Pamungkas and Sumantri K. Risandriya},
title={Naïve Bayes Classifier for Hand Gestures Recognition},
booktitle={Proceedings of the 3rd International Conference on Applied Engineering - Volume 1: ICAE,},
year={2020},
pages={110-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010352601100114},
isbn={978-989-758-520-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Applied Engineering - Volume 1: ICAE,
TI - Naïve Bayes Classifier for Hand Gestures Recognition
SN - 978-989-758-520-3
AU - Simatupang I.
AU - Pamungkas D.
AU - Risandriya S.
PY - 2020
SP - 110
EP - 114
DO - 10.5220/0010352601100114