Literature survey on machine learning techniques for enhancing accuracy of myoelectric hand gesture recognition in real-world prosthetic hand control

Abstract

The human hand, essential for performing daily tasks and facilitating social interaction, is indispensable to everyday life. Millions worldwide experience varying levels of amputation, profoundly affecting their physical, emotional, and psychological well-being, limiting independence, and reducing quality of life. Myoelectric prosthetics, the most advanced active prosthetic hands, use surface electromyography (sEMG) signals and pattern recognition to translate user intentions into control signals. Despite these advancements, high rejection rates persist due to the non-stationarity of sEMG signals, leading to inconsistent and often frustrating user experiences. As a result, clinical and academic research has increasingly focused on improving myoelectric hand gesture recognition under real-world conditions to reduce rejection rates and enhance user acceptance of myoelectric prostheses. Given the vast and diverse range of methods applied in previous research, this survey aims to systematically highlight key studies and provide an overview of the field’s current achievements. Furthermore, research on machine learning for myoelectric hand gesture recognition has been largely influenced by unrelated fields of computer science, such as computer vision and natural language processing. However, myoelectric hand gesture recognition presents unique challenges, particularly severe and unpredictable covariate shifts in sEMG signals, which require specialized approaches. To address these challenges, we propose a new taxonomy for categorizing machine learning models based on feature extraction methods and decision boundary strategies. Additionally, this paper highlights the need for benchmark datasets that accurately reflect real-world conditions and emphasizes the importance of re-evaluating real-time performance, particularly when using long temporal contextual windows. This study concludes with research challenges and future research directions to enhance the accuracy of myoelectric hand gesture recognition using machine learning techniques.

Keywords

Muscle-computer interface / Surface electromyography / Prosthetic hand / Myoelectric control / Deep learning / Domain adaptation / Transfer learning

CRediT authorship contribution statement

Hongquan Le: Writing - review & editing, Conceptualization, Writing - original draft. Marc in Het Panhuis: Supervision, Writing - review & editing, Funding acquisition. Gursel Alici: Writing - review & editing, Project administration, Conceptualization, Supervision, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The research was made possible through financial support from the ARC Centre of Excellence for Electromaterials Science (CE140100012), the ARC-Discovery Project (DP210102911), and the University of Wollongong.

References

[1] N.H.H. Mohamad Hanif, P. Chappell, N. White, A. Cranny, A psychophysical investigation on vibrotactile sensing for transradial prosthesis users, Cogent Eng. 5 (1) (2018) 1539943, http://dx.doi.org/10.1080/23311916.2018.1539943.

[2] H. van Duinen, S.C. Gandevia, Constraints for control of the human hand,J. Physiol. 589 (23) (2011) 5583-5593, http://dx.doi.org/10.1113/jphysiol.2011.217810.

[3] F. Cordella, et al., Literature review on needs of upper limb prosthesis users, Front. Neurosci. 10 (2016) 209, http://dx.doi.org/10.3389/fnins.2016.00209.

[4] A. Sahu, R. Sagar, S. Sarkar, S. Sagar, Psychological effects of amputation: A review of studies from India, Ind. Psychiatry J. 25 (1) (2016) 4, http://dx.doi.org/10.4103/0972-6748.196041.

[5] G. Buckingham, J. Parr, G. Wood, S. Vine, P. Dimitriou, S. Day, The impact of using an upper-limb prosthesis on the perception of real and illusory weight differences, Psychon. Bull. Rev. 25 (4) (2018) 1507-1516, http://dx.doi.org/10.3758/s13423-017-1425-2.

[6] C. Antfolk, M. D’alonzo, B. Rosen, G. Lundborg, F. Sebelius, C. Cipriani, Sensory feedback in upper limb prosthetics, Expert. Rev. Med. Devices 10 (1) (2013) 45-54, http://dx.doi.org/10.1586/erd.12.68.

[7] S.L. Carey, D.J. Lura, M.J. Highsmith, Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review, J. Rehabil. Res. Dev. 52 (3) (2015) http://dx.doi.org/10.1097/JPO.0000000000000159.

[8] M. Rooks, R. Vogel, L. Fleming, Myoelectric prostheses. a long-term follow-up and a study of the use of alternate prostheses, J. Bone Jt. Surg. Am. Vol. 75 (12) (1993) 1781-1789.

[9] A. Cloutier, J. Yang, Design, control, and sensory feedback of externally powered hand prostheses: a literature review, Crit. Rev.^™ Biomed. Eng..

[10] P. Weiner, J. Starke, S. Rader, F. Hundhausen, T. Asfour, Designing prosthetic hands with embodied intelligence: The KIT prosthetic hands, Front. Neurorobot. 16 (2022) 815716, http://dx.doi.org/10.3389/fnbot.2022.815716.

[11] Y. Huang, et al., Human-like dexterous manipulation for anthropomorphic five-fingered hands: A review, Biomim. Intell. Robot. 5 (1) (2025) 100212, http://dx.doi.org/10.1016/j.birob.2025.100212, 2025/03/01.

[12] Y. Li, et al., SoftGrasp: Adaptive grasping for dexterous hand based on multimodal imitation learning, Biomim. Intell. Robot. 5 (2) (2025) 100217, http://dx.doi.org/10.1016/j.birob.2025.100217,2025/06/01/.

[13] X. Ma, J. Zhang, B. Wang, J. Huang, G. Bao, Continuous adaptive gaits manipulation for three-fingered robotic hands via bioinspired fingertip contact events, Biomim. Intell. Robot. 4 (1) (2024) 100144, http://dx.doi.org/10.1016/j.birob.2024.100144,2024/03/01/.

[14] H. Zhou, C. Tawk, G. Alici, A multipurpose human-machine interface via 3D-printed pressure-based force myography, IEEE Trans. Ind. Informatics 20 (6) (2024) 8838-8849, http://dx.doi.org/10.1109/TII.2024.3375376.

[15] Z. Liao, et al., Human-robot interface based on sEMG envelope signal for the collaborative wearable robot, Biomim. Intell. Robot. 3 (1) (2023) 100079, http://dx.doi.org/10.1016/j.birob.2022.100079.

[16] I. Kyranou, S. Vijayakumar, M.S. Erden, Causes of performance degradation in electromyographic pattern recognition in upper limb prostheses, Front. Neurorobotics 12 (2018) 58, http://dx.doi.org/10.3389/fnbot.2018.00058.

[17] B. Stephens-Fripp, M. Jean Walker, E. Goddard, G. Alici, A survey on what Australians with upper limb difference want in a prosthesis: justifica-tion for using soft robotics and additive manufacturing for customized prosthetic hands, Disabil. Rehabil.: Assist. Technol. (2019) 1-8, http://dx.doi.org/10.1080/17483107.2019.1580777.

[18] M.A. Powell, R.R. Kaliki, N.V. Thakor, User training for pattern recognition-based myoelectric prostheses: Improving phantom limb movement consistency and distinguishability, IEEE Trans. Neural Syst. Rehabil. Eng. 22 (3) (2013) 522-532, http://dx.doi.org/10.1109/TNSRE.2013.2279737.

[19] D. Yang, Y. Gu, N.V. Thakor, H. Liu, Improving the functionality, ro-bustness, and adaptability of myoelectric control for dexterous motion restoration, Exp. Brain Res. 237 (2) (2019) 291-311, http://dx.doi.org/10.1007/s00221-018-5441-x.

[20] X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, J. Yang, A framework for hand gesture recognition based on accelerometer and EMG sensors, IEEE Trans. Syst. Man, Cybernetics- Part A: Syst. Humans 41 (6) (2011) 1064-1076, http://dx.doi.org/10.1109/TSMCA.2011.2116004.

[21] X. Zhang, X. Li, O.W. Samuel, Z. Huang, P. Fang, G. Li, Improving the robustness of electromyogram-pattern recognition for prosthetic control by a postprocessing strategy, Front. Neurorobot. 11 (2017) 51, http://dx.doi.org/10.3389/fnbot.2017.00051.

[22] G. Staude, W. Wolf, Objective motor response onset detection in surface myoelectric signals, Med. Eng. Phys. 21 (6-7) (1999) 449-467, http://dx.doi.org/10.1016/S1350-4533(99)00067-3.

[23] Y. Hu, Y. Wong, W. Wei, Y. Du, M. Kankanhalli, W. Geng, A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition, PloS One 13 (10) (2018) e0206049, http://dx.doi.org/10.1371/journal.pone.0206049.

[24] W. Wei, Y. Wong, Y. Du, Y. Hu, M. Kankanhalli, W. Geng, A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface, Pattern Recognit. Lett. 119 (2019) 131-138, http://dx.doi.org/10.1016/j.patrec.2017.12.005.

[25] A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-Bernard, Y. Laurillau, EMG feature evaluation for improving myoelectric pattern recognition robustness, Expert Syst. Appl. 40 (12) (2013) 4832-4840, http://dx.doi.org/10.1016/j.eswa.2013.02.023.

[26] A.H. Al-Timemy, R.N. Khushaba, G. Bugmann, J. Escudero, Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees, IEEE Trans. Neural Syst. Rehabil. Eng. 24 (6) (2015) 650-661, http://dx.doi.org/10.1109/TNSRE.2015.2445634.

[27] R.N. Khushaba, A. Al-Timemy, S. Kodagoda, K. Nazarpour, Combined in-fluence of forearm orientation and muscular contraction on EMG pattern recognition, Expert Syst. Appl. 61 (2016) 154-161, http://dx.doi.org/10.1016/j.eswa.2016.05.031.

[28] I. Ketykó, F. Kovács, K.Z. Varga, Domain adaptation for sEMG-based gesture recognition with recurrent neural networks, 2019, http://dx.doi.org/10.1109/IJCNN.2019.8852018, arXiv preprint arXiv:1901.06958.

[29] A. Ameri, M.A. Akhaee, E. Scheme, K. Englehart, A deep transfer learning approach to reducing the effect of electrode shift in EMG pattern recognition-based control, IEEE Trans. Neural Syst. Rehabil. Eng. (2019) http://dx.doi.org/10.1109/TNSRE.2019.2962189.

[30] R.N. Khushaba, M. Takruri, J.V. Miro, S. Kodagoda, Towards limb position invariant myoelectric pattern recognition using time-dependent spec-tral features, Neural Netw. 55 (2014) 42-58, http://dx.doi.org/10.1016/j.neunet.2014.03.010.

[31] A. Fougner, E. Scheme, A.D. Chan, K. Englehart, Ø. Stavdahl, Resolving the limb position effect in myoelectric pattern recognition, IEEE Trans. Neural Syst. Rehabil. Eng. 19 (6) (2011) 644-651, http://dx.doi.org/10.1109/TNSRE.2011.2163529.

[32] M. Cifrek, S. Tonković, V. Medved, Measurement and analysis of surface myoelectric signals during fatigued cyclic dynamic contractions, Mea-surement 27 (2) (2000) 85-92, http://dx.doi.org/10.1016/S0263-2241(99)00059-7.

[33] N.E. Bunderson, T.A. Kuiken, Quantification of feature space changes with experience during electromyogram pattern recognition control, IEEE Trans. Neural Syst. Rehabil. Eng. 20 (3) (2012) 239-246, http://dx.doi.org/10.1109/TNSRE.2011.2182525.

[34] D. Farina, R. Merletti, D. Stegeman, Biophysics of the generation of EMG signals, Electromyogr.: Physiol. Eng. Noninvasive Appl. (2004) 81-105, http://dx.doi.org/10.1002/9781119082934.ch02.

[35] R. Merletti, H. Hermens, Detection and conditioning of the surface EMG signal, Electromyogr.: Physiol. Eng. Noninvasive Appl. (2004) 107-131, http://dx.doi.org/10.1002/9781119082934.ch03.

[36] G. Yao, T. Lei, J. Zhong, A review of convolutional-neural-network-based action recognition, Pattern Recognit. Lett. 118 (2019) 14-22, http://dx.doi.org/10.1016/j.patrec.2018.05.018.

[37] K. Simonyan, A. Zisserman, Two-stream convolutional networks for action recognition in videos, in: Advances in Neural Information Processing Systems, 2014, pp. 568-576, http://dx.doi.org/10.48550/arXiv.1406.2199.

[38] Z. Teng, G. Xu, R. Liang, M. Li, S. Zhang, Evaluation of synergy-based hand gesture recognition method against force variation for robust myoelectric control, IEEE Trans. Neural Syst. Rehabil. Eng. 29 (2021) 2345-2354, http://dx.doi.org/10.1109/TNSRE.2021.3124744.

[39] A.J. Fuglevand, D.A. Winter, A.E. Patla, Models of recruitment and rate coding organization in motor-unit pools, J. Neurophysiol. 70 (6) (1993) 2470-2488, http://dx.doi.org/10.1152/jn.1993.70.6.2470.

[40] S. Ni, M.A.A. Al-qaness, A. Hawbani, D. Al-Alimi, M. Abd Elaziz, A.A. Ewees, A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends, Appl. Soft Comput. 166 (2024) 112235, http://dx.doi.org/10.1016/j.asoc.2024.112235, 2024/11/01/

[41] M. Atzori, M. Cognolato, H. Müller, Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands, Front. Neurorobot. 10 (2016) 9, http://dx.doi.org/10.3389/fnbot.2016.00009.

[42] R. Soroushmojdehi, S. Javadzadeh, A. Pedrocchi, M. Gandolla, Transfer learning in hand movement intention detection based on surface elec-tromyography signals, Front. Neurosci. 16 (2022) 977328,http://dx.doi.org/10.3389/fnins.2022.977328.

[43] B. Ghojogh, M. Crowley, Linear and quadratic discriminant analysis: tu-torial, 2019, http://dx.doi.org/10.48550/arXiv.1906.02590, arXiv preprint arXiv:1906.02590.

[44] A. Phinyomark, S. Thongpanja, H. Hu, P. Phukpattaranont, C. Limsakul, The usefulness of mean and median frequencies in electromyography analysis, in: Computational Intelligence in Electromyography Analysis-a Perspective on Current Applications and Future Challenges, IntechOpen, 2012.

[45] X. Zhang, L. Huang, H. Niu, Structural design and stiffness matching control of bionic variable stiffness joint for human-robot collaboration, Biomim. Intell. Robot. 3 (1) (2023) 100084, http://dx.doi.org/10.1016/j.birob.2022.100084.

[46] O. Paiss, G.F. Inbar, Autoregressive modeling of surface EMG and its spectrum with application to fatigue, IEEE Trans. Biomed. Eng. BME-34(10) (1987) 761-770, http://dx.doi.org/10.1109/TBME.1987.325918.

[47] G. Li, et al., Multi-view fusion network-based gesture recognition using sEMG data, IEEE J. Biomed. Heal. Informat. (2023) http://dx.doi.org/10.1109/JBHI.2023.3287979.

[48] J.L. Betthauser, et al., Limb position tolerant pattern recognition for myoelectric prosthesis control with adaptive sparse representations from extreme learning, IEEE Trans. Biomed. Eng. 65 (4) (2017) 770-778, http://dx.doi.org/10.1109/TBME.2017.2719400.

[49] H. Huang, H.-B. Xie, J.-Y. Guo, H.-J. Chen, Ant colony optimization-based feature selection method for surface electromyography signals classification, Comput. Biol. Med. 42 (1) (2012) 30-38, http://dx.doi.org/10.1016/j.compbiomed.2011.10.004.

[50] J. Kilby, H.G. Hosseini, Extracting effective features of SEMG using con-tinuous wavelet transform, in: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2006, pp. 1704-1707, http://dx.doi.org/10.1109/IEMBS.2006.260064.

[51] K. Kiatpanichagij, N. Afzulpurkar, Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyogram classification, Biomed. Signal Process. Control. 4 (2) (2009) 127-138, http://dx.doi.org/10.1016/j.bspc.2009.02.004.

[52] F. Duan, L. Dai, W. Chang, Z. Chen, C. Zhu, W. Li, sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform, IEEE Trans. Ind. Electron. 63 (3) (2015) 1923-1934, http://dx.doi.org/10.1109/TIE.2015.2497212.

[53] R.N. Khushaba, A.H. Al-Timemy, A. Al-Ani, A. Al-Jumaily, A framework of temporal-spatial descriptors-based feature extraction for improved myoelectric pattern recognition, IEEE Trans. Neural Syst. Rehabil. Eng. 25(10) (2017) 1821-1831, http://dx.doi.org/10.1109/TNSRE.2017.2687520.

[54] Y. Zou, L. Cheng, Z. Li, A multimodal fusion model for estimating human hand force: Comparing surface electromyography and ultrasound signals, IEEE Robot. Autom. Mag. 29 (4) (2022) 10-24, http://dx.doi.org/10.1109/MRA.2022.3177486.

[55] M.R. Canal, Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals, J. Med. Syst. 34 (1) (2010) 91-94, http://dx.doi.org/10.1007/s10916-008-9219-8.

[56] A. Phinyomark, R.N. Khushaba, E. Ibánez-Marcelo, A. Patania, E. Scheme, G. Petri, Navigating features: a topologically informed chart of elec-tromyographic features space, J. R. Soc. Interface 14 (137) (2017) 20170734, http://dx.doi.org/10.1098/rsif.2017.0734.

[57] K. Nazarpour, A.H. Al-Timemy, G. Bugmann, A. Jackson, A note on the probability distribution function of the surface electromyogram signal, Brain Res. Bull. 90 (2013) 88-91, http://dx.doi.org/10.1016/j.brainresbull.2012.09.012.

[58] P.A. Kaplanis, C.S. Pattichis, D. Zazula, Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders, Med. Biol. Eng. Comput. 48 (8) (2010) 773-781, http://dx.doi.org/10.1007/s11517-010-0629-7.

[59] X. Zhang, X. Ren, X. Gao, X. Chen, P. Zhou, Complexity analysis of surface EMG for overcoming ECG interference toward proportional myoelectric control, Entropy 18 (4) (2016) 106, http://dx.doi.org/10.3390/e18040106.

[60] E. Scheme, K. Englehart, On the robustness of EMG features for pattern recognition based myoelectric control; a multi-dataset comparison, in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2014, pp. 650-653, http://dx.doi.org/10.1109/EMBC.2014.6943675.

[61] M.A. Oskoei, H. Hu, J.Q. Gan, Manifestation of fatigue in myoelectric signals of dynamic contractions produced during playing PC games, in: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2008, pp. 315-318, http://dx.doi.org/10.1109/IEMBS.2008.4649153.

[62] T.W. Beck, et al., Comparison of Fourier and wavelet transform procedures for examining the mechanomyographic and electromyographic frequency domain responses during fatiguing isokinetic muscle actions of the biceps brachii, J. Electromyography Kinesiol. 15 (2) (2005) 190-199, http://dx.doi.org/10.1016/j.jelekin.2004.08.007.

[63] K. Englehart, B. Hudgins, P.A. Parker, M. Stevenson, Classification of the myoelectric signal using time-frequency based representations, Med. Eng. Phys. 21 (6-7) (1999) 431-438, http://dx.doi.org/10.1016/S1350-4533(99)00066-1.

[64] U. Cote-Allard, et al., Deep learning for electromyographic hand gesture signal classification using transfer learning, IEEE Trans. Neural Syst. Rehabil. Eng. 27 (4) (2019) 760-771, http://dx.doi.org/10.1109/TNSRE.2019.2896269.

[65] M.M.-C. Vidovic, H.-J. Hwang, S. Amsüss, J.M. Hahne, D. Farina, K.-R. Müller, Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation, IEEE Trans. Neural Syst. Rehabil. Eng. 24 (9) (2015) 961-970, http://dx.doi.org/10.1109/TNSRE.2015.2492619.

[66] T. Lorrain, N. Jiang, D. Farina, Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses, J. Neuroeng. Rehabil. 8 (1) (2011) 25, http://dx.doi.org/10.1186/1743-0003-8-25.

[67] R.N. Khushaba, A. Al-Jumaily, Channel and feature selection in multifunc-tion myoelectric control, in: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2007, pp. 5182-5185, http://dx.doi.org/10.1109/IEMBS.2007.4353509.

[68] A. Phinyomark, P. Phukpattaranont, C. Limsakul, Feature reduction and selection for EMG signal classification, Expert Syst. Appl. 39 (8) (2012) 7420-7431, http://dx.doi.org/10.1016/j.eswa.2012.01.102.

[69] K. Englehart, B. Hudgins, P. Parker, M. Stevenson, Time-frequency rep-resentation for classification of the transient myoelectric signal, in: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 20 Biomedical Engineering Towards the Year 2000 and beyond (Cat. No. 98CH36286), vol. 5, IEEE, 1998, pp. 2627-2630, http://dx.doi.org/10.1109/IEMBS.1998.745109.

[70] B. Ghojogh, F. Karray, M. Crowley, Fisher and kernel fisher discrimi-nant analysis: tutorial, 2019, http://dx.doi.org/10.48550/arXiv.1906.09436, arXiv preprint arXiv:1906.09436.

[71] S. Liang, et al., Identification of gesture based on combination of raw sEMG and sEMG envelope using supervised learning and univariate feature selection, J. Bionic Eng. 16 (4) (2019) 647-662, http://dx.doi.org/10.1007/s42235-019-0052-1.

[72] A.S.M. Miah, J. Shin, M.A.M. Hasan, Effective features extraction and selection for hand gesture recognition using sEMG signal, Multimedia Tools Appl. (2024) 1-25, http://dx.doi.org/10.1007/s11042-024-19468-2.

[73] H.M. Al-Angari, G. Kanitz, S. Tarantino, C. Cipriani, Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements, Biomed. Signal Process. Control. 27 (2016) 24-31, http://dx.doi.org/10.1016/j.bspc.2016.01.011.

[74] H. Peng, F. Long, C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell. 27 (8) (2005) 1226-1238, http://dx.doi.org/10.1109/TPAMI.2005.159.

[75] J. Liu, X. Li, G. Li, P. Zhou, EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury, Med. Eng. Phys. 36 (7) (2014) 975-980, http://dx.doi.org/10.1016/j.medengphy.2014.04.003.

[76] I. Mesa, A. Rubio, I. Tubia, J. De No, J. Diaz, Channel and feature selection for a surface electromyographic pattern recognition task, Expert Syst. Appl. 41 (11) (2014) 5190-5200, http://dx.doi.org/10.1016/j.eswa.2014.03.014.

[77] M. Atzori, et al., Electromyography data for non-invasive naturally-controlled robotic hand prostheses, Nature 1 (12/23) (2014) http://dx.doi.org/10.1038/sdata.2014.53.

[78] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE 86 (11) (1998) 2278-2324, http://dx.doi.org/10.1109/5.726791.

[79] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016.

[80] A.v.d. Oord, et al., Wavenet: A generative model for raw audio, 2016, http://dx.doi.org/10.48550/arXiv.1609.03499, arXiv preprint arXiv:1609.03499.

[81] W. Geng, Y. Du, W. Jin, W. Wei, Y. Hu, J. Li, Gesture recognition by instantaneous surface EMG images, Sci. Rep. 6 (1) (2016) 36571, http://dx.doi.org/10.1038/srep36571.

[82] P. Koch, H. Phan, M. Maass, F. Katzberg, A. Mertins, Recurrent Neural Network Based Early Prediction of Future Hand Movements, IEEE, 2018, pp. 4710-4713, http://dx.doi.org/10.1109/EMBC.2018.8513145,2018.

[83] N.K. Karnam, S.R. Dubey, A.C. Turlapaty, B. Gokaraju, EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals, Biocybern. Biomed. Eng. 42 (1) (2022) 325-340, http://dx.doi.org/10.1016/j.bbe.2022.02.005.

[84] Y. Wu, B. Zheng, Y. Zhao, Dynamic gesture recognition based on LSTM-CNN, in: 2018 Chinese Automation Congress, CAC, IEEE, 2018, pp. 2446-2450, http://dx.doi.org/10.1109/CAC.2018.8623035.

[85] H. Le, G.M. Spinks, M. in Het Panhuis, G. Alici, Cross-day myo-electric gesture recognition with hybrid multistream CNN-bidirectional LSTM, in: 2025 IEEE International Conference on Mechatronics, ICM’25, Wollongong, Australia, 2025, http://dx.doi.org/10.1109/ICM62621.2025.10934890.

[86] A. Vaswani, et al., Attention is all you need, Adv. Neural Inf. Process. Syst.(2017).

[87] J. D.M.-W.C. Kenton, L.K. Toutanova, Bert: pre-training of deep bidi-rectional transformers for language understanding, in: Proceedings of NaacL-HLT, vol. Minneapolis, Minnesota, 2019, http://dx.doi.org/10.18653/v1/N19-1423.

[88] J. Achiam, et al., Gpt-4 technical report, 2023, http://dx.doi.org/10.48550/arXiv.2303.08774, arXiv preprint arXiv:2303.08774.

[89] A. Dosovitskiy, An image is worth 16x16 words: Transformers for image recognition at scale, 2020, http://dx.doi.org/10.48550/arXiv.2010.11929, arXiv preprint arXiv:2010.11929.

[90] S. Zabihi, E. Rahimian, A. Asif, A. Mohammadi, Trahgr: transformer for hand gesture recognition via electromyography, IEEE Trans. Neural Syst. Rehabil. Eng. (2023) http://dx.doi.org/10.1109/TNSRE.2023.3324252.

[91] S. Shen, X. Wang, F. Mao, L. Sun, M. Gu, Movements classification through sEMG with convolutional vision transformer and stacking ensemble learning, IEEE Sensors J. 22 (13) (2022) 13318-13325, http://dx.doi.org/10.1109/JSEN.2022.3179535.

[92] S. Zabihi, E. Rahimian, A. Asif, A. Mohammadi,Light-weight CNN-attention based architecture for hand gesture recognition via electromyography, in: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, IEEE, 2023, pp. 1-5, http://dx.doi.org/10.1109/ICASSP49357.2023.10096292.

[93] Y. Liu, X. Li, L. Yang, G. Bian, H. Yu, A CNN-transformer hybrid recognition approach for sEMG-based dynamic gesture prediction, IEEE Trans. Instrum. Meas. 72 (2023) 1-16, http://dx.doi.org/10.1109/TIM.2023.3273651.

[94] Z. Wang, J. Yao, M. Xu, M. Jiang, J. Su, Transformer-based network with temporal depthwise convolutions for sEMG recognition, Pattern Recognit. 145 (2024) 109967, http://dx.doi.org/10.1016/j.patcog.2023.109967.

[95] S. Duan, L. Wu, A. Liu, X. Chen,A global-local fusion model exploring temporal-spatial dependence for multimodal hand gesture recognition, IEEE Trans. Med. Robot. Bionics (2025) http://dx.doi.org/10.1109/TMRB.2025.3550646.

[96] L. Hargrove, K. Englehart, B. Hudgins, The effect of electrode dis-placements on pattern recognition based myoelectric control, in: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2006, pp. 2203-2206, http://dx.doi.org/10.1109/IEMBS.2006.260681.

[97] L. Hargrove, K. Englehart, B. Hudgins, A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control, Biomed. Signal Process. Control. 3 (2) (2008) 175-180, http://dx.doi.org/10.1016/j.bspc.2007.11.005.

[98] E. Scheme, A. Fougner, Ø. Stavdahl, A.D. Chan, K. Englehart, Examining the adverse effects of limb position on pattern recognition based myoelectric control, in: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, 2010, pp. 6337-6340, http://dx.doi.org/10.1109/IEMBS.2010.5627638.

[99] J. Liu, D. Zhang, X. Sheng, X. Zhu, Quantification and solutions of arm movements effect on sEMG pattern recognition, Biomed. Signal Process. Control. 13 (2014) 189-197, http://dx.doi.org/10.1016/j.bspc.2014.05.001.

[100] A. Radmand, E. Scheme, K. Englehart, A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes, in: 2014 36th Annual International Confer-ence of the IEEE Engineering in Medicine and Biology Society, IEEE, 2014, pp. 662-667, http://dx.doi.org/10.1109/EMBC.2014.6943678.

[101] X. Wang, H. Yu, S. Kold, O. Rahbek, S. Bai, Wearable sensors for activity monitoring and motion control: A review, Biomim. Intell. Robot. 3 (1)(2023) 100089, http://dx.doi.org/10.1016/j.birob.2023.100089.

[102] R. Tchantchane, H. Zhou, S. Zhang, G. Alici, A review of hand gesture recognition systems based on noninvasive wearable sensors, Adv. Intell. Syst. 5 (10) (2023) 2300207, http://dx.doi.org/10.1002/aisy.202300207.

[103] H.E. Williams, A.W. Shehata, M.R. Dawson, E. Scheme, J.S. Hebert, P.M. Pilarski, Recurrent convolutional neural networks as an approach to position-aware myoelectric prosthesis control, IEEE Trans. Biomed. Eng. 69 (7) (2022) 2243-2255, http://dx.doi.org/10.1109/TBME.2022.3140269.

[104] O.W. Samuel, et al., Resolving the adverse impact of mobility on myoelectric pattern recognition in upper-limb multifunctional prosthe-ses, Comput. Biol. Med. 90 (2017) 76-87, http://dx.doi.org/10.1016/j.compbiomed.2017.09.013.

[105] W. Wei, L. Ren, M. Zhou, Z. Ma, K. Zhao, X. Xu, Improving unimodal sEMG-based pattern recognition through multimodal generative adversarial learning, IEEE Trans. Instrum. Meas. (2025) http://dx.doi.org/10.1109/TIM.2025.3556214.

[106] S. Zhang, H. Zhou, R. Tchantchane, G. Alici, Hand gesture recognition across various limb positions using a multi-modal sensing system based on self-adaptive data-fusion and convolutional neural networks (CNNs), IEEE Sensors J. (2024) http://dx.doi.org/10.1109/JSEN.2024.3389963.

[107] P.R. Young, et al., The effects of limb position and grasped load on hand gesture classification using electromyography, force myography, and their combination, PloS One 20 (4) (2025) e0321319, http://dx.doi.org/10.1371/journal.pone.0321319.

[108] T.T. Oyemakinde, et al., A novel sEMG-FMG combined sensor fusion approach based on an attention-driven CNN for dynamic hand gesture recognition, IEEE Trans. Instrum. Meas. (2025) http://dx.doi.org/10.1109/TIM.2025.3552811.

[109] M. Rohr, et al., On the benefit of FMG and EMG sensor fusion for gesture recognition using cross-subject validation, IEEE Trans. Neural Syst. Rehabil. Eng. (2025) http://dx.doi.org/10.1109/TNSRE.2025.3543649.

[110] J.L. Betthauser, L.E. Osborn, R.R. Kaliki, N.V. Thakor, Electrode-shift toler-ant myoelectric movement-pattern classification using extreme learning for adaptive sparse representations, in: 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS, IEEE, 2017, pp. 1-4, http://dx.doi.org/10.1109/BIOCAS.2017.8325201.

[111] T. Farrell, R.F. Weir,Analysis window induced controller delay for multifunctional prostheses, in:Myoelectric Symposium, 2008.

[112] R. Menon, G. Di Caterina, H. Lakany, L. Petropoulakis, B.A. Conway, J.J. Soraghan, Study on interaction between temporal and spatial information in classification of EMG signals for myoelectric prostheses, IEEE Trans. Neural Syst. Rehabil. Eng. 25 (10) (2017) 1832-1842, http://dx.doi.org/10.1109/TNSRE.2017.2687761.

[113] X. Zhai, B. Jelfs, R.H. Chan, C. Tin, Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network, Front. Neurosci. 11 (2017) 379, http://dx.doi.org/10.3389/fnins.2017.00379.

[114] L.I. Barona López, F.M. Ferri, J. Zea, Á.L. Valdivieso Caraguay, M.E. Be-nalcázar, CNN-LSTM and post-processing for EMG-based hand gesture recognition, Intell. Syst. Appl. 22 (2024) 200352, http://dx.doi.org/10.1016/j.iswa.2024.200352, 2024/06/01.

[115] J.L. Betthauser, J.T. Krall, R.R. Kaliki, M.S. Fifer, N.V. Thakor, Stable elec-tromyographic sequence prediction during movement transitions using temporal convolutional networks, in: 2019 9th International IEEE/EMBS Conference on Neural Engineering, NER, IEEE, 2019, pp. 1046-1049, http://dx.doi.org/10.1109/NER.2019.8717169.

[116] J.L. Betthauser, et al., Stable responsive EMG sequence prediction and adaptive reinforcement with temporal convolutional networks, IEEE Trans. Biomed. Eng. 67 (6) (2019) 1707-1717, http://dx.doi.org/10.1109/TBME.2019.2943309.

[117] S.A. Stuttaford, M. Dyson, K. Nazarpour, S.S. Dupan, Reducing motor variability enhances myoelectric control robustness across untrained limb positions, in: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, http://dx.doi.org/10.1109/TNSRE.2023.3343621.

[118] J. He, D. Zhang, X. Sheng, S. Li, X. Zhu, Invariant surface EMG feature against varying contraction level for myoelectric control based on muscle coordination, IEEE J. Biomed. Heal. Informat. 19 (3) (2014) 874-882, http://dx.doi.org/10.1109/JBHI.2014.2330356.

[119] F. Onay, A. Mert, Phasor represented EMG feature extraction against varying contraction level of prosthetic control, Biomed. Signal Process. Control. 59 (2020) 101881, http://dx.doi.org/10.1016/j.bspc.2020.101881.

[120] F. Zhuang, et al., A comprehensive survey on transfer learning, Proc. IEEE 109 (1) (2020) 43-76, http://dx.doi.org/10.1109/JPROC.2020.3004555.

[121] R.N. Khushaba, Correlation analysis of electromyogram signals for mul-tiuser myoelectric interfaces, IEEE Trans. Neural Syst. Rehabil. Eng. 22 (4)(2014) 745-755, http://dx.doi.org/10.1109/TNSRE.2014.2304470.

[122] F. Kulwa, et al., A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based ges-ture characterization, Biomed. Signal Process. Control. 95 (2024) 106446, http://dx.doi.org/10.1016/j.bspc.2024.106446.

[123] T. Hoshino, S. Kanoga, M. Tsubaki, A. Aoyama, Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers, Neurocomputing 489 (2022) 599-612, http://dx.doi.org/10.1016/j.neucom.2021.12.081.

[124] J. Liu, X. Sheng, D. Zhang, J. He, X. Zhu, Reduced daily recalibration of myoelectric prosthesis classifiers based on domain adaptation, IEEE J. Biomed. Heal. Informat. 20 (1) (2014) 166-176, http://dx.doi.org/10.1109/JBHI.2014.2380454.

[125] K.-T. Kim, C. Guan, S.-W. Lee, A subject-transfer framework based on single-trial EMG analysis using convolutional neural networks, IEEE Trans. Neural Syst. Rehabil. Eng. 28 (1) (2019) 94-103, http://dx.doi.org/10.1109/TNSRE.2019.2946625.

[126] A.A. Rusu, et al., Progressive neural networks, 2016, http://dx.doi.org/10.48550/arXiv.1606.04671, arXiv preprint arXiv:1606.04671.

[127] X. Chen, Y. Li, R. Hu, X. Zhang, X. Chen, Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method, IEEE J. Biomed. Heal. Informat. 25 (4) (2020) 1292-1304, http://dx.doi.org/10.1109/JBHI.2020.3009383.

[128] Y. Zou, L. Cheng, A transfer learning model for gesture recognition based on the deep features extracted by CNN, IEEE Trans. Artif. Intell. 2 (5)(2021) 447-458, http://dx.doi.org/10.1109/TAI.2021.3098253.

[129] P. Shi, X. Zhang, W. Li, H. Yu, Improving the robustness and adaptability of sEMG-based pattern recognition using deep domain adaptation, IEEE J. Biomed. Heal. Informat. 26 (11) (2022) 5450-5460, http://dx.doi.org/10.1109/JBHI.2022.3197831.

[130] J. Yang, D. Cha, D.-G. Lee, S. Ahn, Stcnet: Spatio-temporal cross network with subject-aware contrastive learning for hand gesture recognition in surface EMG, Comput. Biol. Med. 185 (2025) 109525, http://dx.doi.org/10.1016/j.compbiomed.2024.109525.

[131] J. He, N. Jiang, Biometric from surface electromyogram (sEMG): Feasibility of user verification and identification based on gesture recognition, Front. Bioeng. Biotechnol. 8 (2020) 58.

[132] M.H. Zafar, S.K.R. Moosavi, F. Sanfilippo, Federated learning-enhanced edge deep learning model for EMG-based gesture recognition in real-time human-robot interaction, IEEE Sensors J. (2025) http://dx.doi.org/10.1109/JSEN.2025.3529841.

[133] Z. Zhang, Y. Ming, Y. Wang, A federated transfer learning approach for surface electromyographic hand gesture recognition with emphasis on privacy preservation, Eng. Appl. Artif. Intell. 136 (2024) 108952, http://dx.doi.org/10.1016/j.engappai.2024.108952.

[134] H. Lee, M. Jiang, Q. Zhao,FedAssist: Federated learning in AI-powered prosthetics for sustainable and collaborative learning, in: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (EMBC) 15-19 2024, 2024, pp. 1-5, http://dx.doi.org/10.1109/EMBC53108.2024.10781961.

[135] D.R. Hardoon, S. Szedmak, J. Shawe-Taylor, Canonical correlation analysis: An overview with application to learning methods, Neural Comput. 16(12)(2004) 2639-2664, http://dx.doi.org/10.1162/0899766042321814.

[136] B. Fernando, A. Habrard, M. Sebban, T. Tuytelaars,Unsupervised visual domain adaptation using subspace alignment, in:Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2960-2967.

[137] B. Gong, Y. Shi, F. Sha, K. Grauman, Geodesic flow kernel for unsupervised domain adaptation, in: 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2012, pp. 2066-2073, http://dx.doi.org/10.1109/CVPR.2012.6247911.

[138] B. Sun, J. Feng, K. Saenko, Correlation alignment for unsupervised domain adaptation, in: Domain Adaptation in Computer Vision Applications, Springer, 2017, pp. 153-171.

[139] S.J. Pan, I.W. Tsang, J.T. Kwok, Q. Yang, Domain adaptation via transfer component analysis, IEEE Trans. Neural Netw. 22 (2) (2010) 199-210, http://dx.doi.org/10.1109/TNN.2010.2091281.

[140] H. Shi, X. Jiang, C. Dai, W. Chen, EMG-based multi-user hand gesture classification via unsupervised transfer learning using unknown calibra-tion gestures, IEEE Trans. Neural Syst. Rehabil. Eng. 32 (2024) 1119-1131, http://dx.doi.org/10.1109/TNSRE.2024.3372002.

[141] X. Zhang, X. Zhang, L. Wu, C. Li, X. Chen, X. Chen, Domain adaptation with self-guided adaptive sampling strategy: feature alignment for cross-user myoelectric pattern recognition, IEEE Trans. Neural Syst. Rehabil. Eng. 30 (2022) 1374-1383, http://dx.doi.org/10.1109/TNSRE.2022.3173946.

[142] X. Chen, D. Zhang, X. Zhu, Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control, J. NeuroEng. Rehabil. 10 (1) (2013) 44, http://dx.doi.org/10.1186/1743-0003-10-44, 2013/05/01.

[143] S. Amsüss, P.M. Goebel, N. Jiang, B. Graimann, L. Paredes, D. Farina, Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control, IEEE Trans. Biomed. Eng. 61 (4) (2013) 1167-1176, http://dx.doi.org/10.1109/TBME.2013.2296274.

[144] Z. Wang, et al., Optimization of inter-subject sEMG-based hand gesture recognition tasks using unsupervised domain adaptation techniques, Biomed. Signal Process. Control. 92 (2024) 106086, http://dx.doi.org/10.1016/j.bspc.2024.106086.

[145] H. Le, M. in Het Panhuis, G.M. Spinks, G. Alici, The effect of dataset size on EMG gesture recognition under diverse limb positions, in: 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), IEEE, 2024, pp. 303-308, http://dx.doi.org/10.1109/BioRob60516.2024.10719858.

[146] H. Le, M. in Het Panhuis, G.M. Spinks, G. Alici, Quantifying covariate shift and improving electromyography driven gesture recognition with calibration and sample selection, in: 2024 IEEE/ASME International Con-ference on Advanced Intelligent Mechatronics, IEEE, 2024, pp. 1434-1440, http://dx.doi.org/10.1109/AIM55361.2024.10636995.

[147] U. Côté-Allard, E. Campbell, A. Phinyomark, F. Laviolette, B. Gosselin, E. Scheme, Interpreting deep learning features for myoelectric control: A comparison with handcrafted features, Front. Bioeng. Biotechnol. 8 (2020) 158, http://dx.doi.org/10.3389/fbioe.2020.00158.

[148] T.R. Farrell, Determining delay created by multifunctional prosthesis controllers, J. Rehabil. Res. Dev. 48 (6) (2011) xxi.

[149] G. Akkad, A. Mansour, E. Inaty, Embedded deep learning accelerators: A survey on recent advances, IEEE Trans. Artif. Intell. 5 (5) (2023) 1954-1972, http://dx.doi.org/10.1109/TAI.2023.3311776.

[150] A.T. Nguyen, et al., A portable, self-contained neuroprosthetic hand with deep learning-based finger control, J. Neural Eng. 18 (5) (2021) 056051, http://dx.doi.org/10.1088/1741-2552/ac2a8d.

[151] T. Hastie, R. Tibshirani, J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.

[152] B. Frénay, M. Verleysen, Classification in the presence of label noise: a survey, IEEE Trans. Neural Networks Learn. Syst. 25 (5) (2013) 845-869, http://dx.doi.org/10.1109/TNNLS.2013.2292894.

[153] K. Nar, O. Ocal, S.S. Sastry, K. Ramchandran, Cross-entropy loss leads to poor margins, 2018.

[154] R.J. Beaulieu, et al., Multi-position training improves robustness of pattern recognition and reduces limb-position effect in prosthetic control, J. Prosthetics Orthot.: JPO 29 (2) (2017) 54, http://dx.doi.org/10.1097/JPO.0000000000000121.

[155] N. Jiang, I. Vujaklija, H. Rehbaum, B. Graimann, D. Farina, Is accurate map-ping of EMG signals on kinematics needed for precise online myoelectric control? IEEE Trans. Neural Syst. Rehabil. Eng. 22 (3) (2013) 549-558, http://dx.doi.org/10.1109/TNSRE.2013.2287383.

[156] I.S. MacKenzie, C. Ware, Lag as a determinant of human performance in interactive systems, in: Proceedings of the INTERACT’93 and CHI’93 Conference on Human Factors in Computing Systems, ACM, 1993, pp. 488-493, http://dx.doi.org/10.1145/169059.169431.

[157] B. Berberian, P. Le Blaye, C. Schulte, N. Kinani, P.R. Sim, Data transmission latency and sense of control, in: International Conference on Engineering Psychology and Cognitive Ergonomics, Springer, 2013, pp. 3-12, http://dx.doi.org/10.1007/978-3-642-39360-0_1.

[158] P.R. Cavanagh, P.V. Komi, Electromechanical delay in human skeletal muscle under concentric and eccentric contractions, Eur. J. Appl. Physiol. 42 (1979) 159-163, http://dx.doi.org/10.1007/BF00431022.

[159] S. Mehta, M. Rastegari, Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer, 2021, http://dx.doi.org/10.48550/arXiv.2110.02178., arXiv preprint arXiv:2110.02178

[160] A.G. Howard, Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017, http://dx.doi.org/10.48550/arXiv.1704.04861, arXiv preprint arXiv:1704.04861.

[161] A. Moslemi, A. Briskina, Z. Dang, J. Li, A survey on knowledge distillation: Recent advancements, Mach. Learn. Appl. (2024) 100605, http://dx.doi.org/10.1016/j.mlwa.2024.100605.

[162] L. Wei, Z. Ma, C. Yang, Q. Yao, Advances in the neural network quan-tization: A comprehensive review, Appl. Sci. 14 (17) (2024) 7445, http://dx.doi.org/10.3390/app14177445.

[163] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510-4520.

[164] H. Cheng, M. Zhang, J.Q. Shi, A survey on deep neural network pruning: Taxonomy, Comp. Anal. Recomm. IEEE Trans. Pattern Anal. Mach. Intell.(2024) http://dx.doi.org/10.1109/TPAMI.2024.3447085.