ID | 113535 |
著者 |
Hiraoka, Daiki
Yahoo Japan Corporation
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キーワード | Wrist EMG
SVM
Janken Recognition
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資料タイプ |
学術雑誌論文
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抄録 | We propose a method which can discriminate hand motions in this paper. We measure an electromyogram (EMG) of wrist by using 8 dry type sensors. We focus on four motions, such as "Rock-Scissors-Paper" and "Neutral". "Neutral" is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove a hum noise. Next, we combine values of sensors based on a Gaussian function. In this Gaussian function, variance and mean are 0.2 and 0, respectively. We then apply normalization by linear transformation to the values. Subsequently, we resize the values into the range from -1 to 1. Finally, a support vector machine (SVM) conducts learning and discrimination to classify them. We conducted experiments with seven subjects. Average of discrimination accuracy was 89.8%. In the previous method, the discrimination accuracy was 77.1%. Therefore, the proposed method is better in accuracy than the previous method. In future work, we will conduct an experiment which discriminates Japanese Janken of a subject who is not learned.
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掲載誌名 |
ECTI Transactions on Computer and Information Technology
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ISSN | 22869131
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出版者 | Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology Association
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巻 | 11
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号 | 2
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開始ページ | 154
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終了ページ | 162
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発行日 | 2017-12-07
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権利情報 | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License( https://creativecommons.org/licenses/by-nc-nd/4.0/).
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出版社版URL | |
フルテキストファイル | |
言語 |
eng
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著者版フラグ |
出版社版
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部局 |
理工学系
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