Research Article
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Year 2020, , 44 - 55, 15.06.2020

Aysun Tutak Erözen

https://doi.org/10.38088/jise.730957
Cited By: 3

Abstract

References

  • [1] Khezri, M., and Jahed, M. (2007). A novel approach to recognize hand movements via sEMG patterns. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 4907-4910.
  • [2] Alam, M.S., and Arefin, A.S. (2017). Real-Time Classification of Multi-Channel Forearm EMG to Recognize Hand Movements using Effective Feature Combination and LDA Classifier. Bangladesh Journal of Medical Physics, 10(1):25-39.
  • [3] Tello, R.M., Bastos-Filho, T., Costa, R.M., Frizera-Neto, A., Arjunan, S., and Kumar, D. (2013). Towards sEMG classification based on Bayesian and k-NN to control a prosthetic hand. In 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC) pp. 1-6.
  • [4] Atzori, M., Gijsberts, A., Müller, H., and Caputo, B. (2014). Classification of hand movements in amputated subjects by sEMG and accelerometers. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 3545-3549.
  • [5] Palermo, F., Cognolato, M., Gijsberts, A., Müller, H., Caputo, B., & Atzori, M. (2017). Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. In 2017 International Conference on Rehabilitation Robotics (ICORR). pp. 1154-1159.
  • [6] Khokhar, Z.O., Xiao, Z.G., and Menon, C. (2010). Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomedical engineering online, 9(1): 41. https://doi.org/10.1186/1475-925X-9-41. [7] Naik, G.R., Kumar, D.K., Singh, V.P., and Palaniswami, M. (2006). SEMG for identifying hand gestures using ICA. In Workshop on Biosignal Processing and Classification at 2nd International Conference on Informatics in Control, Automation and Roboticsi pp. 61-67.
  • [8] Kyranou, I., Krasoulis, A., Erden, M.S., Nazarpour, K., and Vijayakumar, S. (2016). Real-time classification of multi-modal sensory data for prosthetic hand control. In 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). pp. 536-541.
  • [9] Boostani, R., and Moradi, M.H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological measurement, 24(2): 309-320.
  • [10] Altın, C., and Er, O. (2016). Comparison of different time and frequency domain feature extraction methods on elbow gesture’s EMG. European journal of interdisciplinary studies, 2(3): 35-44.
  • [11] Phinyomark, A., Phukpattaranont, P., and Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert systems with applications, 39(8): 7420-7431.
  • [12] Phinyomark, A., Thongpanja, S., Hu, H., Phukpattaranont, P., and Limsakul, C. (2012). The usefulness of mean and median frequencies in electromyography analysis. Computational intelligence in electromyography analysis-A perspective on current applications and future challenges, 195-220.
  • [13] Mane, S.M., Kambli, R.A., Kazi, F. S., and Singh, N.M. (2015). Hand motion recognition from single channel surface EMG using wavelet & artificial neural network. Procedia Computer Science, 49:58-65.
  • [14] Subasi, A., Yilmaz, M., & Ozcalik, H. R. (2006). Classification of EMG signals using wavelet neural network. Journal of neuroscience methods, 156(1-2):360-367.
  • [15] Sharma, S., and Kumar, G. (2012). Wavelet analysis based feature extraction for pattern classification from Single channel acquired EMG signal. Elixir Online Journal, 50: 0320-1.
  • [16] Raesh, V., and Kumar, P.R. (2009). Hand gestures recognition based on SEMG signal using wavelet and pattern recognisation. International Journal of Recent Trends in Engineering, 1(4): 26-28.
  • [17] Rafiee, J., Rafiee, M.A., Yavari, F., and Schoen, M.P. (2011). Feature extraction of forearm EMG signals for prosthetics. Expert Systems with Applications, 38(4): 4058-4067.
  • [18] Zhong, J., Shi, J., Cai, Y., and Zhang, Q. (2011). Recognition of hand motions via surface EMG signal with rough entropy. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4100-4103.
  • [19] Phinyomark, A., Limsakul, C., and Phukpattaranont, P. (2009). EMG feature extraction for tolerance of 50 Hz interference. In Proc. of PSU-UNS Inter. Conf. on Engineering Technologies, ICET, pp. 289-293.
  • [20] Reaz, M. B. I., Hussain, M. S., and Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1): 11-35.
  • [21] Allard, U.C., Nougarou, F., Fall, C.L., Giguère, P., Gosselin, C., Laviolette, F., and Gosselin, B. (2016). A convolutional neural network for robotic arm guidance using semg based frequency-features. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2464-2470.
  • [22] Cote-Allard, U., Fall, C.L., Campeau-Lecours, A., Gosselin, C., Laviolette, F., and Gosselin, B. (2017). Transfer learning for sEMG hand gestures recognition using convolutional neural networks. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1663-1668.
  • [23] Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., and Geng, W. (2018). A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PloS one, 13(10):1-18.
  • [24] Shen, S., Gu, K., Chen, X.R., Yang, M., and Wang, R.C. (2019). Movements Classification of Multi-Channel sEMG Based on CNN and Stacking Ensemble Learning. IEEE Access, 7, 137489-137500.
  • [25] Chen, L., Fu, J., Wu, Y., Li, H., and Zheng, B. (2020). Hand Gesture Recognition Using Compact CNN Via Surface Electromyography Signals. Sensors, 20(3): 672. 10.3390/s20030672 [26] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A. (2015). Going Deeper With Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1-9., M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1): 11-35.
  • [27] Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725-1732.
  • [28] EMG Data for Gestures Data Set, UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/EMG+data+for+gestures
  • [29] Ahsan, M.R., Ibrahimy, M.I., and Khalifa, O.O. (2009). EMG Signal Classification for Human Computer Interaction: A review. European Journal of Scientific Research, 33(3): 480–501.
  • [30] Benatti, S. (2015). A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition. IEEE Transactions on Biomedical Circuits and Systems, 9(5): 620-630.
  • [31] Pomboza-Junez, G. and Terriza, J.H. (2016). Hand Gesture Recognition Based on sEMG Signals using Support Vector Machines. 2016 IEEE 6th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, 2016, pp. 174-178.
  • [32] Akhmadeev, K., Rampone, E., Yu, T., Aoustin, Y., Carpentier, E.L. (2017). A Testing System for a Real-time Gesture Classification Using Surface EMG. IFAC-PapersOnLine. 50(1): 11498-11503.
  • [33] Amirabdollahian F. and Walters, M.L. (2017). Application of Support Vector Machines in Detecting Hand Grasp Gestures Using A Commercially Off The Shelf Wireless Myoelectric Armband. 2017 International Conference on Rehabilitation Robotics (ICORR), London, 2017, pp. 111-115.
  • [34] Frederic, K., Michael, P., and Antonio, K. (2017). User-independent Real-time Hand Gesture Recognition Based On Surface Electromyography. 1-7. 10.1145/3098279.3098553.
  • [35] Lobov, S., Mironov, V., Kastalskiy, I., and Kazantsev, V. (2015). A Spiking Neural Network in sEMG Feature Extraction. Sensors, 15(11): 27894-27904.
  • [36] Okwelume, G., and Ezeude A.K. (2007). Blind Source Separation Using Frequency Domain Independent Component Analysis. Lambert Academic Publishing. 64 p.

A New CNN Approach for Hand Gesture Classification using sEMG Data

Year 2020, , 44 - 55, 15.06.2020

Aysun Tutak Erözen

https://doi.org/10.38088/jise.730957
Cited By: 3

Abstract

In this paper, a new CNN architecture is introduced for classification of six different hand gestures using surface electromyography (EMG) data collected from the forearm. At first, two different deep neural networks produced based on Slow Fusion and Inception models separately. Then, the average of accuracy values and standard deviations were calculated for each type of network. The average accuracy was 80.88% and standard deviation was 0.030 for the Slow Fusion based network. For the Inception based network, average accuracy was 82.64% and standard deviation was 0.028. In addition to these two networks, a new CNN architecture is introduced using Slow fusion and Inception models in combination. The architecture has two parallel Inception modules in parallel. Each parallel module is fed by the half of the 3D feature map. The proposed model slowly fuses the information of the parallel modules throughout the network as in Slow-Fusion architecture. The average accuracy achieved with this model was 83.97% and the standard deviation was 0.027. Despite the small data set, the accuracy had increased with the proposed hybrid model. The smaller standard deviation indicates that it is less affected by variations in the training dataset. Our experimental results show that the proposed method gives the best results among the Slow Fusion based and Inception based models.

Keywords

EMG CNN Deep Learning Slow Fusion Gesture Recognition Inception

References

  • [1] Khezri, M., and Jahed, M. (2007). A novel approach to recognize hand movements via sEMG patterns. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 4907-4910.
  • [2] Alam, M.S., and Arefin, A.S. (2017). Real-Time Classification of Multi-Channel Forearm EMG to Recognize Hand Movements using Effective Feature Combination and LDA Classifier. Bangladesh Journal of Medical Physics, 10(1):25-39.
  • [3] Tello, R.M., Bastos-Filho, T., Costa, R.M., Frizera-Neto, A., Arjunan, S., and Kumar, D. (2013). Towards sEMG classification based on Bayesian and k-NN to control a prosthetic hand. In 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC) pp. 1-6.
  • [4] Atzori, M., Gijsberts, A., Müller, H., and Caputo, B. (2014). Classification of hand movements in amputated subjects by sEMG and accelerometers. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 3545-3549.
  • [5] Palermo, F., Cognolato, M., Gijsberts, A., Müller, H., Caputo, B., & Atzori, M. (2017). Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. In 2017 International Conference on Rehabilitation Robotics (ICORR). pp. 1154-1159.
  • [6] Khokhar, Z.O., Xiao, Z.G., and Menon, C. (2010). Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomedical engineering online, 9(1): 41. https://doi.org/10.1186/1475-925X-9-41. [7] Naik, G.R., Kumar, D.K., Singh, V.P., and Palaniswami, M. (2006). SEMG for identifying hand gestures using ICA. In Workshop on Biosignal Processing and Classification at 2nd International Conference on Informatics in Control, Automation and Roboticsi pp. 61-67.
  • [8] Kyranou, I., Krasoulis, A., Erden, M.S., Nazarpour, K., and Vijayakumar, S. (2016). Real-time classification of multi-modal sensory data for prosthetic hand control. In 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). pp. 536-541.
  • [9] Boostani, R., and Moradi, M.H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological measurement, 24(2): 309-320.
  • [10] Altın, C., and Er, O. (2016). Comparison of different time and frequency domain feature extraction methods on elbow gesture’s EMG. European journal of interdisciplinary studies, 2(3): 35-44.
  • [11] Phinyomark, A., Phukpattaranont, P., and Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert systems with applications, 39(8): 7420-7431.
  • [12] Phinyomark, A., Thongpanja, S., Hu, H., Phukpattaranont, P., and Limsakul, C. (2012). The usefulness of mean and median frequencies in electromyography analysis. Computational intelligence in electromyography analysis-A perspective on current applications and future challenges, 195-220.
  • [13] Mane, S.M., Kambli, R.A., Kazi, F. S., and Singh, N.M. (2015). Hand motion recognition from single channel surface EMG using wavelet & artificial neural network. Procedia Computer Science, 49:58-65.
  • [14] Subasi, A., Yilmaz, M., & Ozcalik, H. R. (2006). Classification of EMG signals using wavelet neural network. Journal of neuroscience methods, 156(1-2):360-367.
  • [15] Sharma, S., and Kumar, G. (2012). Wavelet analysis based feature extraction for pattern classification from Single channel acquired EMG signal. Elixir Online Journal, 50: 0320-1.
  • [16] Raesh, V., and Kumar, P.R. (2009). Hand gestures recognition based on SEMG signal using wavelet and pattern recognisation. International Journal of Recent Trends in Engineering, 1(4): 26-28.
  • [17] Rafiee, J., Rafiee, M.A., Yavari, F., and Schoen, M.P. (2011). Feature extraction of forearm EMG signals for prosthetics. Expert Systems with Applications, 38(4): 4058-4067.
  • [18] Zhong, J., Shi, J., Cai, Y., and Zhang, Q. (2011). Recognition of hand motions via surface EMG signal with rough entropy. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4100-4103.
  • [19] Phinyomark, A., Limsakul, C., and Phukpattaranont, P. (2009). EMG feature extraction for tolerance of 50 Hz interference. In Proc. of PSU-UNS Inter. Conf. on Engineering Technologies, ICET, pp. 289-293.
  • [20] Reaz, M. B. I., Hussain, M. S., and Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1): 11-35.
  • [21] Allard, U.C., Nougarou, F., Fall, C.L., Giguère, P., Gosselin, C., Laviolette, F., and Gosselin, B. (2016). A convolutional neural network for robotic arm guidance using semg based frequency-features. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2464-2470.
  • [22] Cote-Allard, U., Fall, C.L., Campeau-Lecours, A., Gosselin, C., Laviolette, F., and Gosselin, B. (2017). Transfer learning for sEMG hand gestures recognition using convolutional neural networks. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1663-1668.
  • [23] Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., and Geng, W. (2018). A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PloS one, 13(10):1-18.
  • [24] Shen, S., Gu, K., Chen, X.R., Yang, M., and Wang, R.C. (2019). Movements Classification of Multi-Channel sEMG Based on CNN and Stacking Ensemble Learning. IEEE Access, 7, 137489-137500.
  • [25] Chen, L., Fu, J., Wu, Y., Li, H., and Zheng, B. (2020). Hand Gesture Recognition Using Compact CNN Via Surface Electromyography Signals. Sensors, 20(3): 672. 10.3390/s20030672 [26] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A. (2015). Going Deeper With Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1-9., M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1): 11-35.
  • [27] Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725-1732.
  • [28] EMG Data for Gestures Data Set, UCI Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/EMG+data+for+gestures
  • [29] Ahsan, M.R., Ibrahimy, M.I., and Khalifa, O.O. (2009). EMG Signal Classification for Human Computer Interaction: A review. European Journal of Scientific Research, 33(3): 480–501.
  • [30] Benatti, S. (2015). A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition. IEEE Transactions on Biomedical Circuits and Systems, 9(5): 620-630.
  • [31] Pomboza-Junez, G. and Terriza, J.H. (2016). Hand Gesture Recognition Based on sEMG Signals using Support Vector Machines. 2016 IEEE 6th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, 2016, pp. 174-178.
  • [32] Akhmadeev, K., Rampone, E., Yu, T., Aoustin, Y., Carpentier, E.L. (2017). A Testing System for a Real-time Gesture Classification Using Surface EMG. IFAC-PapersOnLine. 50(1): 11498-11503.
  • [33] Amirabdollahian F. and Walters, M.L. (2017). Application of Support Vector Machines in Detecting Hand Grasp Gestures Using A Commercially Off The Shelf Wireless Myoelectric Armband. 2017 International Conference on Rehabilitation Robotics (ICORR), London, 2017, pp. 111-115.
  • [34] Frederic, K., Michael, P., and Antonio, K. (2017). User-independent Real-time Hand Gesture Recognition Based On Surface Electromyography. 1-7. 10.1145/3098279.3098553.
  • [35] Lobov, S., Mironov, V., Kastalskiy, I., and Kazantsev, V. (2015). A Spiking Neural Network in sEMG Feature Extraction. Sensors, 15(11): 27894-27904.
  • [36] Okwelume, G., and Ezeude A.K. (2007). Blind Source Separation Using Frequency Domain Independent Component Analysis. Lambert Academic Publishing. 64 p.
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Aysun Tutak Erözen 0000-0002-6551-4008

Publication Date June 15, 2020
Published in Issue Year 2020

Cite

APA Tutak Erözen, A. (2020). A New CNN Approach for Hand Gesture Classification using sEMG Data. Journal of Innovative Science and Engineering, 4(1), 44-55. https://doi.org/10.38088/jise.730957
AMA Tutak Erözen A. A New CNN Approach for Hand Gesture Classification using sEMG Data. JISE. June 2020;4(1):44-55. doi:10.38088/jise.730957
Chicago Tutak Erözen, Aysun. “A New CNN Approach for Hand Gesture Classification Using SEMG Data”. Journal of Innovative Science and Engineering 4, no. 1 (June 2020): 44-55. https://doi.org/10.38088/jise.730957.
EndNote Tutak Erözen A (June 1, 2020) A New CNN Approach for Hand Gesture Classification using sEMG Data. Journal of Innovative Science and Engineering 4 1 44–55.
IEEE A. Tutak Erözen, “A New CNN Approach for Hand Gesture Classification using sEMG Data”, JISE, vol. 4, no. 1, pp. 44–55, 2020, doi: 10.38088/jise.730957.
ISNAD Tutak Erözen, Aysun. “A New CNN Approach for Hand Gesture Classification Using SEMG Data”. Journal of Innovative Science and Engineering 4/1 (June 2020), 44-55. https://doi.org/10.38088/jise.730957.
JAMA Tutak Erözen A. A New CNN Approach for Hand Gesture Classification using sEMG Data. JISE. 2020;4:44–55.
MLA Tutak Erözen, Aysun. “A New CNN Approach for Hand Gesture Classification Using SEMG Data”. Journal of Innovative Science and Engineering, vol. 4, no. 1, 2020, pp. 44-55, doi:10.38088/jise.730957.
Vancouver Tutak Erözen A. A New CNN Approach for Hand Gesture Classification using sEMG Data. JISE. 2020;4(1):44-55.

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