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目的 表面肌電信號可以直接反映用戶的動作意圖，近年來已經(jīng)成為手勢識別等人機(jī)交互任務(wù)的主要控制信號。然而，個(gè)體差異性使得用戶模型不能通用，限制了其應(yīng)用與發(fā)展。為了解決這個(gè)問題，論文提出了一種新的跨個(gè)體對抗適應(yīng)網(wǎng)絡(luò)(cross-subject adversarial adaptation network, CAAN)。方法 該網(wǎng)絡(luò)包括特征編碼器、手勢分類器和個(gè)體分類器3個(gè)子模塊，使用了新的對抗性適應(yīng)訓(xùn)練方法訓(xùn)練網(wǎng)絡(luò)，達(dá)到分離出個(gè)體私有特征的目標(biāo)。CAAN網(wǎng)絡(luò)在采集的數(shù)據(jù)集上進(jìn)行訓(xùn)練和測試，數(shù)據(jù)集包括11名受試者的6種手勢。結(jié)果 實(shí)驗(yàn)結(jié)果表明，方法的手勢識別準(zhǔn)確率達(dá)到88.08%，通過比較，該方法的性能優(yōu)于現(xiàn)有的方法。結(jié)論 本文提出的CAAN網(wǎng)絡(luò)可有效進(jìn)行跨個(gè)體手勢識別，為人機(jī)交互提供可靠的技術(shù)。

[1] Zhang Z, Yang K, Qian J, et al. Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network[J]. Sensors, 2019, 19(14): 3170.?
[2] Zhang Z, He C, Yang K. A novel surface electromyographic signal-based hand gesture prediction using a recurrent neural network[J]. Sensors, 2020, 20(14): 3994.
[3] Geng W, Du Y, Jin W, et al. Gesture recognition by instantaneous surface EMG images[J]. Scientific Reports, 2016, 6(1): 36571.
[4] Hu Y, Wong Y, Wei W, et al. A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition[J]. PLoS One, 2018, 13(10): e0206049.
[5] C?té-Allard U, Fall CL, Drouin A, et al. Deep learning for electromyographic hand gesture signal classification using transfer learning[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(4): 760-771.
[6] Chen X, Li Y, Hu R, et al. Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 25(4): 1292-1304.
[7] Yasen M, Jusoh S. A systematic review on hand gesture recognition techniques, challenges and applications[J]. PeerJ Computer Science, 2019, 5: e218.
[8] Xie B, Meng J, Li B, et al. Biosignal-based transferable attention Bi-ConvGRU deep network for hand-gesture recognition towards online upper-limb prosthesis control[J]. Computer Methods and Programs in Biomedicine, 2022, 224: 106999.
[9] Ganin Y, Ustinova E, Ajakan H, et al. Domain-adversarial training of neural networks[J]. The Journal of Machine Learning Research, 2016, 17(1): 2096-2030.
[10] Karpathy A, Toderici G, Shetty S, et al. Large-scale video classification with convolutional neural networks[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020). Seattle, WA, USA: IEEE Press, 2014: 1725-1732.
[11] Benalcázar ME, Motoche C, Zea JA, et al. Real-time hand gesture recognition using the myo armband and muscle activity detection[C]//2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM). Salinas, Ecuador: IEEE Press, 2017: 1-6.
[12] Atzori M, Cognolato M, Müller H. Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands[J]. Frontiers in Neurorobotics, 2016, 10: 9.
[13] Colli-Alfaro JG, Ibrahim A, Trejos AL. Design of user-independent hand gesture recognition using multilayer perceptron networks and sensor fusion techniques[C]// 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). Toronto, ON, Canada: IEEE Press, 2019: 1103-1108.
[14] Du Y, Jin W, Wei W, et al. Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation[J]. Sensors, 2017, 17(3): 458.
[15] Kim M, Chung WK, Kim K. Subject-independent sEMG pattern recognition by using a muscle source activation model[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 5175-5180.