Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

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Daiki Hiraoka
Momoyo Ito
Shin-ichi Ito
Minoru Fukumi


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.

Article Details

How to Cite
D. Hiraoka, M. Ito, S.- ichi Ito, and M. Fukumi, “Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist”, ECTI-CIT, vol. 11, no. 2, pp. 154-162, Dec. 2017.
Artificial Intelligence and Machine Learning (AI)


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