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Surface EMG Real-Time Chinese Language Recognition Using Artificial Neural Networks

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Intelligent Life System Modelling, Image Processing and Analysis (LSMS 2021, ICSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1467))

Abstract

Surface electromyography (sEMG) has recently been commonly used in sign language recognition owing to its numerous advantages (i.e., portability, lightweight, and ease of use) over cameras and inertial sensors. We proposed a real-time Chinese sign language recognition model based on sEMG signals in this research. sEMG data was collected using MYO armband on nine healthy volunteers who performed a series of 15 gestures. The signal was preprocessed using a sliding window method followed by a muscle detection operation. Five time-domain features were extracted from the preprocessed signal for feature extraction. We used a feed-forward Artificial Neural Network (ANN) to classify the signals with an activation time threshold to recognize the gestures. This model is 94.9% in accuracy and reacts in around 195.19 ms (real-time).

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References

  1. Gao, W., Fang, G., Zhao, D., Chen, Y.: A Chinese sign language recognition system based on SOFM/SRN/HMM. J. Pattern Recognit. 37, 2389–2402 (2004)

    Article  Google Scholar 

  2. Liu, K., Kehtarnavaz, N.: Real-time robust vision-based hand gesture recognition using stereo images. J. Real-Time Image Proc. 11(1), 201–209 (2013). https://doi.org/10.1007/s11554-013-0333-6

    Article  Google Scholar 

  3. Benalcazar, M.E., et al.: Real-time hand gesture recognition using the Myo armband and muscle activity detection. In: Proceedings of the 2017 IEEE 2nd Ecuador Technical Chapters Meeting, pp. 1--6. Salinas, Ecuador (2017)

    Google Scholar 

  4. Benalcazar, M.E., Jaramillo, A.G., Zea, A., Paez, A., Andaluz, V.H.: Hand gesture recognition using machine learning and the Myo armband. In: Proceedings of the European Signal Processing Conference, pp. 1075--1079. Kos, Greece (2017)

    Google Scholar 

  5. Adib, F., Hsu, C., Mao, H., Katabi, D., Durand, F.: Capturing the human figure through a wall. J. ACM Trans. Graph. 34, 1–13 (2015)

    Google Scholar 

  6. Rossi, M., Benatti, S., Farella, E., Benini, L.: Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics. In: 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 1700–1705. Seville, Spain (2015)

    Google Scholar 

  7. Motoche, C., Benalcázar, M.E.: Real-time hand gesture recognition based on electromyographic signals and artificial neural networks. In: Proceedings of the International Conference on Artificial Neural Networks, pp.4–7, Rhodes, Greece (2018)

    Google Scholar 

  8. Joshi, A., Monnier, C., Betke, M., Sclaroff, S.: Comparing random forest approaches to segmenting and classifying gestures. Image Vis. J. Comput. 58, 86–95 (2017)

    Article  Google Scholar 

  9. Asif, A.R., et al.: Performance evaluation of convolutional neural network for hand gesture recognition using EMG. J. Sens. 20, 1642 (2020)

    Article  Google Scholar 

  10. Mizuno, H., Tsujiuchi, N., Koizumi, T.: Forearm motion discrimination technique using real-time emg signals. In: Engineering in Medicine and Biology Society. EMBC, Annual International Conference of the IEEE, pp. 4435–4438. IEEE (2011)

    Google Scholar 

  11. Zhang, Z., Yang, K., Qian, J., Zhang, L.: Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network. J. Sens. (Basel). 19(14), 3170 (2019)

    Google Scholar 

  12. Zhang, Z., Su, Z., Yang, G.: Real-time Chinese sign language recognition based on artificial neural networks. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1413–1417, Dali, China, (2019)

    Google Scholar 

  13. Benalcázar, M.E., Anchundia, C.E., Zea, J.A., Zambrano, P., Jaramillo, A.G., Segura, M.: Real-time hand gesture recognition based on artificial feed-forward neural networks and EMG. In: 2018 26th European Signal Processing Conference (EUSIPCO), pp. 1492–1496, Rome, Italy (2018)

    Google Scholar 

  14. Kundu, A.S., Mazumder, O., Lenka, P.K., Bhaumik, S.: Hand gesture recognition based omnidirectional wheelchair control using IMU and EMG sensors. J. raml. Robot. Syst. 3, 1–13 (2017)

    Google Scholar 

  15. Wahid, M.F., Tafreshi, R., Al-Sowaidi, M., Langari, R.: Subject-independent hand gesture recognition using normalization and machine learning algorithms. J. Comput. SCI-NETH 27, 69–76 (2018)

    Google Scholar 

  16. Coteallard, U., et al.: Deep learning for electromyographic hand gesture signal classification using transfer learning. J. IEEE Trans. Neural Syst. Rehabil. Eng. 27(4), 760–771 (2019)

    Google Scholar 

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

    M. Majid Riaz & Zhen Zhang

  2. School of Computer Engineering and Sciences, Shanghai University, Shanghai, 200444, China

    M. Majid Riaz & Zhen Zhang

Authors
  1. M. Majid Riaz
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  2. Zhen Zhang
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Corresponding author

Correspondence to Zhen Zhang .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Minrui Fei

  2. University of Tokyo, Tokyo, Japan

    Luonan Chen

  3. Shanghai University, Shanghai, China

    Shiwei Ma

  4. Shanghai University, Shanghai, China

    Xin Li

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Riaz, M.M., Zhang, Z. (2021). Surface EMG Real-Time Chinese Language Recognition Using Artificial Neural Networks. In: Fei, M., Chen, L., Ma, S., Li, X. (eds) Intelligent Life System Modelling, Image Processing and Analysis. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1467. Springer, Singapore. https://doi.org/10.1007/978-981-16-7207-1_12

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  • DOI: https://doi.org/10.1007/978-981-16-7207-1_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7206-4

  • Online ISBN: 978-981-16-7207-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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