Main Article Content
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.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
 Utku Ş. Yavuz, Francesco Negro, Oğuz Sebik, Aleŝ Holobar, Cornelius Frömmel, Kemal S. Türker, Dario Farina: Estimating reflex responses in large populations of motor units by decomposition of the high-density surface electromyogram, The Journal of Physiology, p.4305-4318, August 2015.
 Dario Farina, Ales Holobar: Human Machine Interfacing by Decoding the Surface Electromyogram, IEEE Signal Processing Magazine, volume 32, p.115-120, January 2015.
 Angkoon Phinyomark, Chusak Limsakul, and Pornchai Phukpattaranont: A Novel Feature Extraction for Robust EMG Pattern Recognition, Journal of Computing, Vol.1, Issue 1, pp. 71-80, December 2009
 Osamu Fukuda, Toshio Tsuji, Makoto Kaneko, and Akira Otsuka: A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions, IEEE Trans. on Robotics and Automation, Vol.19, No.2, pp.210-222, April 2003
 Taro Shibanoki, Keisuke Shima, Takeshi Takaki Yuichi Kurita, Akira Otsuka, Takaaki Chin and Toshio Tsuji: Multi-channel Surface EMG Classiﬁcation Based on a Wuasi-optimal Selection of Motions and Channels, International Conference on Complex Medical Engineering, p.276-279, Japan, July 2012.
 Nan Bu, Osamu Fukuda, and Toshio Tsuji: EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network, Journal of Intelligent Information System, 21:2, pp.113-126, 2003
 H. Kawamoto, S. Lee, S. Kanbe, Y. Sankai: “ Power assist method for HAL-3 using EMG-based feedback controller”, IEEE International Conference on Systems, Man and Cybernetics 2003, Vol. 2, pp. 1648-1653, 2003.
 Jeong-Su Han, Z. Zenn Bien, Dae-Jin Kim, Hyong-Euk Lee, Jong-Sung Kim: Human-machine interface for wheelchair control with EMG and its evaluation, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1602–1605, September 2003
 Hsiu-Jen Liu and Kuu-Young Young: An Adaptive Upper-Arm EMG-Based Robot Control System, International Journal of Fuzzy Systems, Vol. 12, No. 3, pp.181-189, September 2010
 Tadahiro Oyama, Yuji Matsumura, Stephen Githinji Karungaru, Yasue Mitsukura and Minoru Fukumi: Construction of Wrist Motion recognition System, Proceedings of 2006 RISP International Workshop on Nonlinear Circuits and Signal Processing, pp. 385-388, Honolulu, March 2006
 Tadahiro Oyama, Stephen Githinji Karungaru, Satoru Tsuge, Yasue Mitsukura and Minoru Fukumi: Fast Approximate Incremental Learning Algorithm based on Simple-FLDA, Journal of Signal Processing, Vol.13, No.6, 515-523, November 2009
 Takahide Funabashi, Yohei Takeuchi, Momoyo Ito, Koji Kashihara and Minoru Fukumi: Recognition of Finger Motion by Wrist EMG, Proceeding of 2012 International Workshop on Nonlinear Circuits, Communication and Signal Processing NCSP’12, p.433-436, Honolulu, March 2012.
 Tadahiro Oyama, Hillary Kipsang Choge, Stephen Githinji Karungaru, Satoru Tsuge and Minoru Fukumi: Wrist EMG Signals Identification using Neural Network, Proc. of the IEEE Industrial Electronics Society 2009, pp.4322-4326, Porto, Nov. 2009
 Kiminobu Sato: Recognition System for Wrist Behavior using DSP Unit and Its Application to Machine Control, Master thesis, Kochi University of Technology, Feb. 2005 (in Japanese)
 Oisaka Electronic Equipment Ltd, Personal-EMG, https://www.oisaka.co.jp/P-EMG.html
 Matthew partridge and Rafael A. Clavo: Fast Dimensionality Reduction and Simple PCA, Intelligent Data Analysis, Vol.2, Issue1-4, pp.203-214, 1998
 Yanzhao Chen, Yiqi Zhou, Xiangli Cheng and Yongzhen Mi: Upper Limb Motion Recognition Based on Two-Step SVM Classiﬁcation Method of Surface EMG, International Journal of Control and Automation, Vol.6, No.3, June 2013.
 Ming-Chang Lee and Chang To: Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress, International Journal of Artiﬁcial Intelligence & Applications (IJAIA), Vol.1, No.3 pp.31-43, 2010.
 Oisaka Electronic Equipment Ltd, P-EMG plus, https://www.oisaka.co.jp/p-emgplus.html
 R. Romero, E. L. Iglesias, and L. Borrajo: A Linear-RBF Multikernel SVM to Classify Big Text Corpora, BioMed Research International, Volume 2015 (2015), Article ID 878291