Abstract
Gesture recognition based on surface electromyographic (sEMG) is one of the most critical technologies in the field of human–computer interactions(HCI). The previous sEMG-based gesture recognition frameworks have poor effect due to the inappropriate feature processing. In this paper, we propose a sEMG-based gesture recognition framework called TF-LSTMCNN(LSTM and CNN based on TD and FD features). We formulated the feature combination from time domain (TD) and frequency domain (FD) as the diversified feature representation, as well as an advanced feature representation learned from a fusion technique that integrates a variety of discriminative sEMG signal patterns. To generate diversity feature representations, we considered the redundancy and uniqueness among features and chose a combination of TD and FD features. The accuracy of the proposed method achieved 85.13%, and is 8.58% higher than SVM which showed higher performance in the machine learning (ML) methods.
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References
Atzori, M., Cognolato, M., Muller, H.: Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front Neurorobot. 10, 9 (2016)
Bahador, A., Yousefi, M., Marashi, M., Bahador, O.: High accurate lightweight deep learning method for gesture recognition based on surface electromyography. Comput. Methods Programs Biomed. 195, 105643 (2020)
Benatti, S., Montagna, F., Kartsch, V., Rahimi, A., Rossi, D., Benini, L.: Online learning and classification of emg-based gestures on a parallel ultra-low power platform using hyperdimensional computing. IEEE Trans. Biomed. Circ. Syst. 13(3), 516–528 (2019)
Chen, H., Zhang, Y., Li, G., Fang, Y., Liu, H.: Surface electromyography feature extraction via convolutional neural network. Int. J. Mach. Learn. Cybernet. 11(1), 185–196 (2019). https://doi.org/10.1007/s13042-019-00966-x
Chen, Y., Zhou, Y., Cheng, X., Mi, Y.: Upper limb motion recognition based on two-step svm classification method of surface emg. Int. J. Control Autom. 6(3), 249–266 (2013)
Ding, Q.C., Xiong, A.B., Zhao, X.G., Han, J.D.: A review on researches and applications of semg-based motion intent recognition methods. Acta Automatica Sinica (2016)
Hu, R., Chen, X., Zhang, X., Chen, X.: Adaptive electrode calibration method based on muscle core activation regions and its application in myoelectric pattern recognition. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 11–20 (2020)
Yu, H., Wong, Y., Dai, Q., Kankanhalli, M., Geng, W., Li, X.: semg-based gesture recognition with embedded virtual hand poses adversarial learning. IEEE Access 7, 104108–104120 (2019)
Jiang, S., et al.: Feasibility of wrist-worn, real-time hand, and surface gesture recognition via semg and imu sensing. IEEE Trans. Ind. Inform. 14(8), 3376–3385 (2018)
Li, Y., Zhang, Q., Zeng, N., Chen, J., Zhang, Q.: Discrete hand motion intention decoding based on transient myoelectric signals. IEEE Access 7, 81630–81639 (2019)
Pan, T.Y., Tsai, W.L., Chang, C.Y., Yeh, C.W., Hu, M.C.: A hierarchical hand gesture recognition framework for sports referee training-based emg and accelerometer sensors. IEEE Transactions on Cybernetics (2020)
Qi, J., Jiang, G., Li, G., Sun, Y., Tao, B.: Intelligent human-computer interaction based on surface emg gesture recognition. IEEE Access 7, 61378–61387 (2019)
Song, W., et al.: Design of a flexible wearable smart semg recorder integrated gradient boosting decision tree based hand gesture recognition. IEEE Trans. Biomed. Circ. Syst. 13(6), 1563–1574 (2019)
Wahid, M.F., Tafreshi, R., Langari, R.: A multi-window majority voting strategy to improve hand gesture recognition accuracies using electromyography signal. IEEE Trans. Neural Syst. Rehabil. Eng. 28(2), 427–436 (2020)
Wu, J., Sun, L., Jafari, R.: A wearable system for recognizing american sign language in real-time using imu and surface emg sensors. IEEE J. Biomed. Health Inform. 20(5), 1281–1290 (2016)
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Luo, Y. et al. (2021). A Fusion Framework to Enhance sEMG-Based Gesture Recognition Using TD and FD Features. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_20
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DOI: https://doi.org/10.1007/978-3-030-92310-5_20
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