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ID 113535
著者
Hiraoka, Daiki Yahoo Japan Corporation
キーワード
Wrist EMG
SVM
Janken Recognition
資料タイプ
学術雑誌論文
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.
掲載誌名
ECTI Transactions on Computer and Information Technology
ISSN
22869131
出版者
Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology Association
11
2
開始ページ
154
終了ページ
162
発行日
2017-12-07
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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言語
eng
著者版フラグ
出版社版
部局
理工学系