- Volume 19 Issue 10
DOI QR Code
A Consistency Study of CNN's Learning to Recognize Korean Finger Number using sEMG Signals
표면근전도 신호를 활용한 한국 숫자지화 인식에서 CNN 학습의 일관성에 관한 연구
- Park, Jong-Jun (Department of Medical IT Engineering, Soonchunhyang University) ;
- Kwon, Chun-Ki (Department of Medical IT Engineering, Soonchunhyang University)
- Received : 2018.07.25
- Accepted : 2018.10.05
- Published : 2018.10.31
Convolutional Neural Network (CNN) has been actively employed in the application of computer vision, and has been proved to have its superior performance in image classification, document classification, and finger print recognition. This work focuses on an application of CNN, having outstanding performance in image classification, to recognition of korean finger number using time series sEMG signals as input and validates CNN's capability in providing its consistent learning in repeated application for recognition of sEMG based Korean finger numbers, which has been rarely a topic in previous studies. To this end, 252 sEMG signals as input data and 108 sEMG signals as test data out of 360 sEMG signals (60 signals each number) acquired from a forearm muscle of the subject who is trained to consistently perform six Korean finger number gestures from zero(0) to five(5) were used for CNN based finger number recognition. CNN was set to have 100 learning iterations for each application of finger number recognition, and to have 10 repetitive applications of finger number recognition for the consistency of CNN's learning. Recognition rate at each repetition was calculated from test data. As can be seen from the results in this work, CNN shows consistent learning at each repetitive application of finger number recognition and outstanding recognition rates of more than 99.1% (missed one case out of 60 cases). Thus, CNN is one of powerful techniques for finger number recognition based on time-series sEMG signals to provide not only global solution but also excellent recognition rates.
convolutional neural network;time-series signal;surface electromyography;Korean finger number gesture recognition;consistency in cnn learning;repeated recognition application
Supported by : 순천향대학교
- O. Abdel-Hamid, A. R. Mohamed, H. Jiang, L. Deng, G. Penn, D. Yu, "Convolutional Neural Networks for speech recognition", IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol.22, No.10, pp.1533-1545, 2014. DOI: https://dx.doi.org/10.1109/TASLP.2014.2339736 https://doi.org/10.1109/TASLP.2014.2339736
- W. Shang, K. Sohn, D. Almeida, H. Lee, "Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear units", Proceedings of the 33rd International Conference on Machine Learning, New York, USA, 2016, arXiv:1603.05201v2
- C. Zhang, K. Qiao, L. Wang, L. Tong, Y. Zeng, B. Yan, "Constraint-Free Natural Image Reconstruction From fMRI Signals Based on Convolutional Neural Network", Frontiers in Human Neruoscience, Vol.12, 2018. DOI: https://dx.doi.org/10.3389/fnhum.2018.00242
- A. Mahendran, A. Vedaldi, "Visualizing Deep Convolutional Neural Networks Using Natural Pre-images", International Journal of Computer Vision, Vol.120, No.3, pp.233-255, 2016. DOI: http://dx.doi.org/10.1007/s11263-016-0911-8 https://doi.org/10.1007/s11263-016-0911-8
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelow, D. Erhan, V. Vanhoucke, A. Rabinovich, "Going deeper with convolutions", Proceedings of Computer Vision and Pattern Recognition, pp.1-9, 2015. DOI: https://dx.doi.org/10.1109/CVPR.2015.7298594
- P. Li, F. Zhao, Y. Li, Z. Zhu, "Law text classification using semi-supervised convolutional neural networks", Proceedings of 2018 Chinese Control And Decision Conference, pp.309-313, 2018. DOI: https://dx.doi.org/10.1109/CCDC.2018.8407150
- V. S. Kulkarni, S. D. Lokhande, "Appearance Based Recognition of American Sign Language Using Gesture Segmentation", International Journal on Computer Science and Engineering, Vol.2, No.3, pp.560-565, 2010.
- H. D. Yang, S. H. Lee, "Automatic Extraction of Sign Language and Finger Gestures for Continuous Recognition", Journal of Electrical Engineering and Information Science: Software and Its application, Vol.38, No.2, pp.102-107, 2011.
- N. H. Kim, "A Development of the Next-generation Interface System Based on the Finger Gesture Recognizing in Use of Image Process Techniques", Journal of the Korea Institute of Information and Communication Engineering, Vol.15, No.4, pp.935-942, 2011. DOI: https://dx.doi.org/10.6109/jkiice.2011.15.4.935 https://doi.org/10.6109/jkiice.2011.15.4.935
- H.S. Kang, "Finger Sign Recognition Technique using sEMG Sensor and Gyro Sensor", Master Thesis, Soongsil University, 2002.
- J. M. Hahne, B. Graimann, K. R. Muller, "Spatial Filtering for Robust Myoelectric Control", IEEE Transactions on Biomedical Engineering, Vol.59, No.5, pp.1436-1443, 2012. DOI: https://dx.doi.org/10.1109/tbme.2012.2188799 https://doi.org/10.1109/TBME.2012.2188799
- A. Phinyomark, P. Phukpattaranont, C. Limsakul, "A Review of Control Methods for Electric Power Wheelchairs Based on Electromyography Signals with Special Emphasis on Pattern Recognition", IETE Technical Review, Vol.28, No.4, pp.316-326, 2011. DOI: https://dx.doi.org/10.4103/0256-4602.83552 https://doi.org/10.4103/0256-4602.83552
- P. J. Lin, H. Y. Chen, "Design and implement of a rehabilitation system with surface electromyography technology", Proceedings of 2018 IEEE International Conference on Applied System Invention (ICASI), pp.513-515, 2018. DOI: https://dx.doi.org/10.1109/icasi.2018.8394300
- O. S. Powar, K. Chemmangat, "Feature selection for myoelectric pattern recognition using two channel surface electromyography signals", Proceedings of TENCON 2017 - 2017 IEEE Region 10 Conference, pp.1022-1026, 2017. DOI: https://dx.doi.org/10.1109/tencon.2017.8228007
- A. J. Young, L. J. Hargrove, T. A. Kuiken, "Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration", IEEE Transactions on Biomedical Engineering, Vol.59, No.3, pp.645-652, 2012. DOI: https://dx.doi.org/10.1109/tbme.2011.2177662 https://doi.org/10.1109/TBME.2011.2177662
- L. Pan, D. Zhang, N. Jiang, X. Sheng, X. Zhu, "Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns", Journal of NeuroEngineering and Rehabilitation, Vol.12, No.110, pp.1-16, 2015. DOI: https://dx.doi.org/10.1186/s12984-015-0102-9 https://doi.org/10.1186/1743-0003-12-1
- X. Xun, Z. J. Wang, "Pattern recognition of number gestures based on a wireless surface EMG system", Biomedical Signal Processing and Control, Vol.8, No.2, pp.184-192, 2013. DOI: https://dx.doi.org/10.1016/j.bspc.2012.08.005 https://doi.org/10.1016/j.bspc.2012.08.005
- X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, J. Yang, "A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors", IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol.41, No.6, pp.1064-1076, 2011. DOI: https://dx.doi.org/10.1109/tsmca.2011.2116004 https://doi.org/10.1109/TSMCA.2011.2116004
- J. J. Park, "Study on sEMG Signal based Finger Sign Recognition using Convolutional Neural Network", Master Thesis, Soonchunhyang University, 2017.
- Active Two User Manual Version 3.2, Biosemi, 2007
- Faculty Association of Korea Anatomy and Physiology, "Human Anatomy", Hyunmoonsa, pp.214-218, 2009.
- M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, "Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems", in CORR, Vol.abs/160.04467, March 2016.