Abstract
With the continuous development of EMG acquisition technology and artificial intelligence technology, EMG signal analysis has been extensively studied in human–computer interaction, rehabilitation training, prosthetic control and remote device control. As hand movements become more and more complex, hand movement recognition based on surface electromyography (sEMG) has become a hotspot. In this paper, by using multi-features fusion-based Long Short-Term Memory convolutional neural network (MFFCNN-LSTM), a continuous hand movement recognition method based on time-domain and time–frequency-spectrum features of forearm sEMG signal is proposed. Ten basic hand movements including rest action are identified. Firstly, the hand movement data is cut from NinaPro db8 dataset to extract effective sEMG signal fragments. Secondly, the empirical Fourier decomposition method is used to denoise the sEMG signals. Thirdly, the time-domain and time–frequency-spectrum features of sEMG signals from different channels are extracted, and sent to two parallel CNN networks to extract the high-dimension features, respectively. Fourthly, the high-dimension features are fused as the input of LSTM, a fully connected layer and a softmax layer to recognize the continuous hand movements. Finally, MFFCNN-LSTM is compared with the support vector machine, CNN and LSTM on the same computer. The experimental results show that the recognition accuracy, sensitivity and specificity of MFFCNN-LSTM on NinaPro db8 dataset are 98.5%, 95.25% and 95.5%, respectively. It has higher recognition accuracy on the five public datasets than other methods.
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References
Park, H., An, B., Baek, J., Lee, D., Seo, J.: Selection of grasping target and control system of robotic prosthetic hand using images and deep learning. J. Inst. Control 26(5), 312–317 (2020)
Rheem, H., Becker, D.V., Craig, S.D.: Assessing learning effort with hand motion tracking methods. Appl. Cogn. Psychol. 35, 606–620 (2021). https://doi.org/10.1002/acp.3784
Vishwakarma, D.K., and Grover, V.: Hand gesture recognition in low-intensity environment using depth images, in Editor (Ed.)^(Eds.): Book Hand gesture recognition in low-intensity environment using depth images, 429–433(2017, edn.)
Jain, R., Karsh, R.K., Barbhuiya, A.A.: Encoded motion image-based dynamic hand gesture recognition. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02259-3
Zhang W, Wang J, Lan F (2021) Dynamic hand gesture recognition based on short-term sampling neural networks, IEEE/CAA J. Autom. Sin., 8, (01), 114-124. DOI: https://doi.org/10.1109/JAS.2020.1003465
Tan, Y.S., Lim, K.M., Tee, C., Lee, C.P., Low, C.Y.: Convolutional neural network with spatial pyramid pooling for hand gesture recognition. Neural Comput. Appl. 33, 5339–5351 (2021). https://doi.org/10.1007/s00521-020-05337-0
Li, H., Wu, L., Wang, H., Han, C., Quan, W., Zhao, J.: Hand gesture recognition enhancement based on spatial fuzzy matching in leap motion. IEEE Trans. Ind. Inf. 16(3), 1885–1894 (2020). https://doi.org/10.1109/TII.2019.2931140
Wang, B., Li, Y., Lang, H., Wang, Y.: Hand gesture recognition and motion estimation using the kinect sensor. Control. Intell. Syst. 48(1), 17–24 (2020). https://doi.org/10.2316/J.2020.201-0014
Lee, C., Kim, J., Cho, S., Kim, J., and Kwon, S.: Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect, Sensors, 20(16), 4566(2020) DOI: https://doi.org/10.3390/s20164566
Bilal, S., Akmeliawati, R., Shafie, A.A., Salami, M.J.E.: Hidden Markov model for human to computer interaction: a study on human hand gesture recognition. Artif. Intell. Rev. 40(4), 495–516 (2013). https://doi.org/10.1007/s10462-011-9292-0
Chen, G., and Ge, K.: A fusion recognition method based on multifeature hidden markov model for dynamic hand gesture, Comput. Intell. Neurosci., 2020 (12), 8871605(2020). https://doi.org/10.1155/2020/8871605
Jaramillo-Yanez, A., Unapanta, L., and Benalcázar, M.E.: Short-Term Hand Gesture Recognition using Electromyography in the Transient State, Support Vector Machines, and Discrete Wavelet Transform, in Editor (Ed.)^(Eds.): Book Short-Term Hand Gesture Recognition using Electromyography in the Transient State, Support Vector Machines, and Discrete Wavelet Transform (2020, edn.)
Winarno, H.A., Poernama, A.I., Soesanti, I., and Nugroho, H.A.: Evaluation on EMG electrode reduction in recognizing the pattern of hand gesture by using SVM Method, J. Phys. Conf. Ser., 1577 (1), 012044 (2019)
Ahsan, M.R., Ibrahimy, M.I., and Khalifa, O.O.: Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN), in Editor (Ed.)^(Eds.): Book Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN) (2011, edn.)
Saeed, B., Zia-ur-Rehman, M., Gilani, S.O., Amin, F., Waris, A., Jamil, M., Shafique, M.: Leveraging ANN and LDA Classifiers for characterizing different hand movements using EMG signals. Arab. J. Sci. Eng. 46, 1761–1769 (2021). https://doi.org/10.1007/s13369-020-05044-x
Pradeep Kumar, B.P., Manjunatha, M.B.: Performance analysis of KNN, SVM and ANN techniques for gesture recognition system, Indian J. Sci. Technol., 9, (S1)(2016). DOI: https://doi.org/10.17485/ijst/2016/v9is1/111145
Pinzón-Arenas, J.O., Jiménez-Moreno, R., and Herrera-Benavides, J.E.: Convolutional neural network for hand gesture recognition using 8 different EMG Signals, in Editor (Eds.): Book Convolutional neural network for hand gesture recognition using 8 different EMG Signals (2019, edn.)
Velandia, N.S., Moreno, R.J., Rubiano, A.: CNN architectures for hand gesture recognition using EMG signals throw wavelet feature extraction. J. Eng. Appl. Sci. 14(11), 3528–3537 (2019). https://doi.org/10.36478/jeasci.2019.3528.3537
Asif, A.R., Waris, A., Gilani, S.O., Jamil, M., Ashraf, H., Shafique, M., and Niazi, I.K.: Performance evaluation of convolutional neural network for hand gesture recognition using EMG, Sensors (Basel, Switzerland), 20(6)(2020). DOI: https://doi.org/10.3390/s20061642
Kolla, B.P.: Accurate hand gesture recognition using CNN and RNN approaches. Int. J. Adv. Trends Computer Sci. Eng. 9(3), 3216–3222 (2020). https://doi.org/10.30534/ijatcse/2020/114932020
Xing, Y., Caterina, G.D., Soraghan, J.: A new spiking convolutional recurrent neural network (SCRNN) with applications to event-based hand gesture recognition. Front. Neurosci. 14, 590164 (2020). https://doi.org/10.3389/fnins.2020.590164
Jabbari, M., Khushaba, R.N., and Nazarpour, K.: EMG-based hand gesture classification with long short-term memory deep recurrent neural networks, 2020 42nd annual international conference of the IEEE engineering in medicine and biology society (EMBC), 3302–3305(2020)
LeCun, Boser, Denker, Henderson, Howard, Hubbard, and Jackel: Backpropagation applied to handwritten Zip Code Recognition, Neural Comput. (1989). DOI: https://doi.org/10.1162/neco.1989.1.4.541
Chen, H.F., Tong, R.Z., Chen, M.J., Fang, Y.F., Liu, H.H., and Ieee: A hybrid cnn-svm classifier for hand gesture recognition with surface emg signals: Proceedings of 2018 International Conference on Machine Learning and Cybernetics (Ieee, 2018), 619–624(2018).
Too, J., Abdullah, A.R., Saad, N.M., Ali, N.M., Zawawi, T.N.S.T.: Deep convolutional neural network for featureless electromyogram pattern recognition using time-frequency distribution. Sens. Lett. 16(8), 92–99 (2018). https://doi.org/10.1166/sl.2018.3926
Jordan, M.I. (1986) Attractor Dynamics and parallellism in a connectionist sequential machine, Proc.annu.conf.of the Cognitive Science Society Amherst Ma, 531–546.
Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowledge Based Syst. 06(02), 107–116 (1998). https://doi.org/10.1142/S0218488598000094
Krasoulis, A., Vijayakumar, S., Nazarpour, K.: Effect of user practice on prosthetic finger control with an intuitive myoelectric decoder. Front. Neurosci. 13, 891 (2010). https://doi.org/10.3389/fnins.2019.00891
Astuti, W., Sediono, W., Aibinu, A.M., Akmeliawati, R., and Salami, M.J.E.: Adaptive Short Time Fourier Transform (STFT) Analysis of seismic electric signal (SES): A comparison of Hamming and rectangular window, 2012 IEEE Symposium on Industrial Electronics and Applications, 372–379 (2012)
Sbrollini, A., Agostinelli, A., Di Nardo, F., Maranesi, E., Mengarelli, A., Fioretti, S., and Burattini, L.: Evaluation of the Low-Frequency Components in Surface Electromyography, in Patton, J., Barbieri, R., Ji, J., Jabbari, E., Dokos, S., Mukkamala, R., Guiraud, D., Jovanov, E., Dhaher, Y., Panescu, D., Vangils, M., Wheeler, B., and Dhawan, A.P. (Eds.): 2016 38th Annual International Conference of the Ieee Engineering in Medicine and Biology Society , 3622–3625 (2016)
Singh, P., Joshi, S.D., Patney, R.K., Saha, K.: The Fourier decomposition method for nonlinear and non-stationary time series analysis Proc. R. Soc. A. 473, 20160871 (2017). https://doi.org/10.1098/rspa.2016.0871
Zhou W , Feng Z, Xu Y F , et al. Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method, arXiv: Signal Processing: (2020)
Ahmed, Bamakhramah, Soliman, A., Mahmoud, Saeed, A., and Al-Tunaiji: Six Order Cascaded Power Line Notch Filter for ECG Detection Systems with Noise Shaping, Circuits Syst. Signal Process 33, 2385–2400 (2014). https://doi.org/10.1007/s00034-014-9761-1
Huang, Norden E., Shen, Samuel S.P.: Hilbert-Huang transform and its applications. World Scientific. 16 (2014). https://doi.org/10.1142/5862
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Wang, C., Guo, W., Zhang, H., Guo, L., Lin, C.: sEMG-based continuous estimation of grasp movements by long-short term memory network. Biomed. Signal Process. Control 59, 101774 (2020). https://doi.org/10.1016/j.bspc.2019.101774
W. Chen, Z. Zhang: Hand gesture recognition using sEMG signals based on support vector machine. 2019 IEEE 8th joint international information technology and artificial intelligence conference (ITAIC), 230–234(2019). DOI: https://doi.org/10.1109/ITAIC.2019.8785542
Erzen, A.T.: A new CNN approach for hand gesture classification using sEMG data. J. Innovative Sci. Eng. (JISE) 4(1), 44–55 (2020). https://doi.org/10.38088/jise.730957
Fatimah, B., Singh, P., Singhal, A., et al.: Hand movement recognition from sEMG signals using Fourier decomposition method. Biocybernetics Biomed. Eng. 2(41), 690–703 (2021). https://doi.org/10.1016/j.bbe.2021.03.004
Phinyomark, A., Scheme, E.: EMG pattern recognition in the era of big data and deep learning. Big Data Cogn. Comput. 2, 21 (2018). https://doi.org/10.3390/bdcc2030021
Lobov S., Krilova N., Kastalskiy I., Kazantsev V., Makarov V.A.: Latent factors limiting the performance of sEMG-interfaces. Sensors. 18(4), 1122. DOI: https://doi.org/10.3390/s18041122
Saetta, G., Cognolato, M., Atzori, M., et al.: Gaze, behavioral, and clinical data for phantom limbs after hand amputation from 15 amputees and 29 controls. Sci. Data 7, 60 (2020). https://doi.org/10.1038/s41597-020-0402-1
Du Y, Jin W, Wei W, Hu Y, Geng W.: Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation. Sensors (Basel). 17(3), 458(2017). DOI: https://doi.org/10.3390/s17030458
Georgi, M., Amma, C., Schultz, T.: Recognizing hand and finger gestures with IMU based Motion and EMG based Muscle Activity Sensing. 99–108 (2015). https://doi.org/10.5220/0005276900990108
Kaczmarek, P., Mańkowski, T., Tomczyński, J., putEMG-A Surface Electromyography Hand Gesture Recognition Dataset. Sensors (Basel, Switzerland), 19(16), 3548(2019). DOI: https://doi.org/10.3390/s19163548
Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1401200; in part by the Key Project of Hebei Province Department of Education under Grant ZD2020146; in part by the Hebei Province Postdoctoral Scientific Research Project under Grant B2019005001; in part by the Program for Top 100 Innovative Talents in Colleges and Universities of Hebei Province under grant SLRC2017022; and in part by the Natural Science Foundation of China under grant 61703133 and 61673158.
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Li, J., Wei, L., Wen, Y. et al. An approach to continuous hand movement recognition using SEMG based on features fusion. Vis Comput 39, 2065–2079 (2023). https://doi.org/10.1007/s00371-022-02465-7
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DOI: https://doi.org/10.1007/s00371-022-02465-7