Publication: Armband Gesture Recognition on Electromyography Signal for Virtual Control
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Issued Date
2018-08-06
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2-s2.0-85052312463
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Mahidol University
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SCOPUS
Bibliographic Citation
2018 10th International Conference on Knowledge and Smart Technology: Cybernetics in the Next Decades, KST 2018. (2018), 149-153
Suggested Citation
Tanasanee Phienthrakul Armband Gesture Recognition on Electromyography Signal for Virtual Control. 2018 10th International Conference on Knowledge and Smart Technology: Cybernetics in the Next Decades, KST 2018. (2018), 149-153. doi:10.1109/KST.2018.8426118 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/45605
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Title
Armband Gesture Recognition on Electromyography Signal for Virtual Control
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Abstract
© 2018 IEEE. Many new devices come out with the idea of making more comfortable life. Myo armband is a wireless device for interacting with computer using electromyography (EMG) sensor. To communicate with the computer, the poses of hand and arm are matched with the command to control like a mouse click. Although the standard Myo can be used to communicate with computer, some poses cannot be detected or their results may be wrong. In this paper, the machine learning techniques will be applied to detect the hand gestures or poses. Double-tap, fist, spread finger, wavein, and wave-out are 5 basic poses. These basic poses and rest will be trained and tested. The experimental results show that RBF network yields the acceptable results when it is compared to the results of many techniques.