Gesture classification based on high-density surface electromyography (HD-sEMG) is a key research area for assisting prosthetic users in rehabilitation. The existing algorithms have practical applications, although their efficiency and convenience may still require improvement. This study proposes an improved fuzzy adaptive membership function algorithm (IFAMF) to enhance classification accuracy and system robustness. Following data preprocessing, four types of features were extracted from different perspectives. During feature fuzzification, a reinforcement learning algorithm was employed to adaptively select the membership function type, with parameters determined via gradient descent, and these parameters were further refined using particle swarm optimization. Five well-established and reliable classifiers were selected for classification, and their performance was evaluated using a publicly available dataset. The experimental results demonstrated that under single-feature classification, the accuracy improved from 78.89% to 88.09%, with an average increase of 9.20%. Under the optimal feature combination, the final average accuracy reached 92.69%, with performance improvements across the classifiers ranging from 3.08% to 10.36%. These findings validate the superiority of the proposed method and suggest a promising direction for its integration with more advanced classifiers and feature extraction techniques to achieve more precise and intelligent prosthetic control.

1. [1] E. Bergil, C. Oral, and E. U. Ergul, “Efficient Hand Movement Detection Using k-Means Clustering and k-Nearest Neighbor Algorithms,” J. Med. Biol. Eng., Vol.41, pp. 11-24, 2021. https://doi.org/10.1007/s40846-020-00537-4
2. [2] C. M. Light, P. H. Chappell, B. Hudgins, and K. Engelhart, “Intelligent multifunction myoelectric control of hand prostheses,” J. Med. Eng. Technol., Vol.26, Issue 4, pp. 139-146, 2002. https://doi.org/10.1080/03091900210142459
3. [3] T. J. Bates, J. R. Fergason, and S. N. Pierrie, “Technological Advances in Prosthesis Design and Rehabilitation Following Upper Extremity Limb Loss,” Curr. Rev. Musculoskelet. Med., Vol.13, pp. 485-493, 2020. https://doi.org/10.1007/s12178-020-09656-6
4. [4] P. Geethanjali, “Myoelectric control of prosthetic hands: State-of-the-art review,” Med. Devices: Evid. Res., Vol.9, pp. 247-255, 2016. https://doi.org/10.2147/MDER.S91102
5. [5] N. J. Jarque-Bou, J. L. Sancho-Bru, and M. Vergara, “A systematic review of EMG applications for the characterization of forearm and hand muscle activity during activities of daily living: Results, challenges, and open issues,” Sensors, Vol.21, Issue 9, Article No.3035, 2021. https://doi.org/10.3390/s21093035
6. [6] M. Nowak, I. Vujaklija, A. Sturma et al., “Simultaneous and Proportional Real-Time Myocontrol of Up to Three Degrees of Freedom of the Wrist and Hand,” IEEE Trans. Biomed. Eng., Vol.70, Issue 2, pp. 459-469, 2022. https://doi.org/10.1109/TBME.2022.3194104
7. [7] H. Huang, T. A. Kuiken, and R. D. Lipschutz, “A Strategy for Identifying Locomotion Modes Using Surface Electromyography,” IEEE Trans. Biomed. Eng., Vol.56, Issue 1, pp. 65-73, 2008. https://doi.org/10.1109/TBME.2008.2003293
8. [8] D. Farina, R. Merletti, and R. M. Enoka, “The extraction of neural strategies from the surface EMG: An update,” J. Appl. Physiol., Vol.117, Issue 11, pp. 1215-1230, 2014. https://doi.org/10.1152/japplphysiol.00162.2014
9. [9] H. Xu and A. Xiong, “Advances and Disturbances in sEMG-Based Intentions and Movements Recognition: A Review,” IEEE Sens. J., Vol.21, Issue 12, pp. 13019-13028, 2021. https://doi.org/10.1109/JSEN.2021.3068521
10. [10] W. Li, P. Shi, and H. Yu, “Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future,” Front. Neurosci., Vol.15, Article No.621885, 2021. https://doi.org/10.3389/fnins.2021.621885
11. [11] F. Duan, X. Ren, and Y. Yang, “A Gesture Recognition System Based on Time Domain Features and Linear Discriminant Analysis,” IEEE Trans. Cogn. Dev. Syst., Vol.13, Issue 1, pp. 200-208, 2018. https://doi.org/10.1109/TCDS.2018.2884942
12. [12] M. Tavakoli, C. Benussi, P. A. Lopes et al., “Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier,” Biomed. Signal Process. Control, Vol.46, pp. 121-130, 2018. https://doi.org/10.1016/j.bspc.2018.07.010
13. [13] G. R. Naik and D. K. Kumar, “Twin SVM for gesture classification using the surface electromyogram,” IEEE Trans. Inf. Technol. Biomed., Vol.14, Issue 2, pp. 301-308, 2009. https://doi.org/10.1109/TITB.2009.2037752
14. [14] M. Pourmokhtari and B. Beigzadeh, “Simple recognition of hand gestures using single-channel EMG signals,” Proc. Inst. Mech. Eng. H., Vol.238, Issue 3, pp. 372-380, 2024. https://doi.org/10.1177/09544119231225528
15. [15] Q. Zhang and F. Deng, “Dynamic Gesture Recognition Based on LeapMotion and HMM-CART Model,” J. Phys.: Conf. Ser., Vol.910, Article No.012037, 2017. https://doi.org/10.1088/1742-6596/910/1/012037
16. [16] Y. Zhang, Z. Yang, G. Zhang et al., “XGest: Enabling Cross-Label Gesture Recognition with RF Signals,” ACM Trans. Sens. Netw., Vol.17, No.4, Article No.37, 2021. https://doi.org/10.1145/3458750
17. [17] J. Zhao, J. Mao, G. Wang et al., “A miniaturized wearable wireless hand gesture recognition system employing deep-forest classifier,” Proc. IEEE Biomed. Circuits Syst. Conf. (BioCAS), 2017. https://doi.org/10.1109/BIOCAS.2017.8325161
18. [18] A. A. Adewuyi, L. J. Hargrove, and T. A. Kuiken, “Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control,” Front. Neurorobot., Vol.10, Article No.15, 2016. https://doi.org/10.3389/fnbot.2016.00015
19. [19] D. Na, L.-Z. Liu, X- J. Yu et al., “Classification of multichannel surface-electromyography signals based on convolutional neural networks,” J. Ind. Inf. Integr., Vol.15, pp. 201-206, 2019. https://doi.org/10.1016/j.jii.2018.09.001
20. [20] W. Wei, Q. Dai, Y. Wong et al., “Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning,” IEEE Trans. Biomed. Eng., Vol.66, Issue 10, pp. 2964-2973, 2019. https://doi.org/10.1109/TBME.2019.2899222
21. [21] Q. Li, Z. Luo, and J. Zheng, “A New Deep Anomaly Detection-Based Method for User Authentication Using Multichannel Surface EMG Signals of Hand Gestures,” IEEE Trans. Instrum. Meas., Vol.71, 2022. https://doi.org/10.1109/TIM.2022.3164162
22. [22] W. Wei, H. Hong, and X. Wu, “A Hierarchical View Pooling Network for Multichannel Surface Electromyography-Based Gesture Recognition,” Comput. Intell. Neurosci., Vol.2021, Article No.6591035, 2021. https://doi.org/10.1155/2021/6591035
23. [23] J. Maiers and Y. S. Sherif, “Applications of fuzzy set theory,” IEEE Trans. Syst. Man Cybern., Vol.15, Issue 1, pp. 175-189, 1985. https://doi.org/10.1109/TSMC.1985.6313408
24. [24] R.-E. Precup and H. Hellendoorn, “A survey on industrial applications of fuzzy control,” Comput. Ind., Vol.62, Issue 3, pp. 213-226, 2011. https://doi.org/10.1016/j.compind.2010.10.001
25. [25] A. B. McBratney and I. O. A. Odeh, “Application of fuzzy sets in soil science: Fuzzy logic, fuzzy measurements and fuzzy decisions,” Geoderma, Vol.77, Issues 2-4, pp. 85-113, 1997. https://doi.org/10.1016/S0016-7061(97)00017-7
26. [26] F. H. Y. Chan, Y. S. Yang, F. K. Lam et al., “Fuzzy EMG classification for prosthesis control,” IEEE Trans. Rehabil. Eng., Vol.8, Issue 3, pp. 305-311, 2000. https://doi.org/10.1109/86.867872
27. [27] A. B. Ajiboye and R. F. Weir, “A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol.13, Issue 3, pp. 280-291, 2005. https://doi.org/10.1109/TNSRE.2005.847357
28. [28] H. B. Xie, H. Huang, J. Wu, and L. Liu, “A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine,” Physiol. Meas., Vol.36, No.2, Article No.191, 2015. https://doi.org/10.1088/0967-3334/36/2/191
29. [29] F. AlOmari and G. Liu, “Analysis of Extracted Forearm sEMG Signal Using LDA, QDA, K-NN Classification Algorithms,” Open Autom. Control Syst. J., Vol.6, No.1, pp. 108-116, 2014. http://doi.org/10.2174/1874444301406010108
30. [30] Y. Chen, Z. Cai, L. Shi, and W. Li, “A fuzzy granular sparse learning model for identifying antigenic variants of influenza viruses,” Appl. Soft Comput., Vol.109, Article No.107573, 2021. https://doi.org/10.1016/j.asoc.2021.107573
31. [31] Y. Diao, Q. Chen, Y. Liu et al., “A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification,” J. Neural Eng., Vol.20, No.2, Article No.026029, 2023. https://doi.org/10.1088/1741-2552/acc42a
32. [32] N. Malešević, A. Olsson, P. Sager et al., “A database of high-density surface electromyogram signals comprising 65 isometric hand gestures,” Sci. Data, Vol.8, Article No.63, 2021. https://doi.org/10.1038/s41597-021-00843-9
33. [33] L. I. B. López, Á. L. V. Caraguay, V. H. Vimos et al., “An Energy-Based Method for Orientation Correction of EMG Bracelet Sensors in Hand Gesture Recognition Systems,” Sensors, Vol.20, Issue 21, Article No.6327, 2020. https://doi.org/10.3390/s20216327
34. [34] M. A. Ozdemir, D. H. Kisa, O. Guren et al., “EMG based Hand Gesture Recognition using Deep Learning,” Proc. 2020 Medical Technologies Congress (TIPTEKNO), 2020. https://doi.org/10.1109/TIPTEKNO50054.2020.9299264
35. [35] G. Jia, H.-K. Lam, J. Liao, and R. Wang, “Classification of electromyographic hand gesture signals using machine learning techniques,” Neurocomputing, Vol.401, pp. 236-248, 2020. https://doi.org/10.1016/j.neucom.2020.03.009
36. [36] M. J. H. Erazo Macias and S. A. Vega, “Electromyographic Pattern Analysis and Classification for a Robotic Prosthetic Arm,” Applied Bionics and Biomechanics, Vol.3, Issue 2, pp. 113-120, 2006. https://doi.org/10.1533/abbi.2005.0039
37. [37] P. Zhang, “Model selection via multifold cross validation,” Ann. Stat., Vol.21, No.1, pp. 299-313, 1993. https://doi.org/10.1214/aos/1176349027
38. [38] R. A. Ramadan, “Clustering Based Fuzzy Logic for Multimodal Sensor Networks: A Preprocessing to Decision Fusion,” J. of Ambient Intelligence and Smart Environments, Vol.2, Issue 3, pp. 271-286, 2010. https://doi.org/10.3233/AIS-2010-0072
39. [39] Q. Li and R. Langari, “Myoelectric Human Computer Interaction Using CNN-LSTM Neural Network for Dynamic Hand Gestures Recognition,” 2021 IEEE Int. Conf. on Big Data (Big Data), pp. 5947-5949, 2021. https://doi.org/10.1109/BigData52589.2021.9671283
40. [40] M. Montazerin, “Deep Learning Methods for Hand Gesture Recognition via High-Density Surface Electromyogram (HD-sEMG) Signals,” Ph.D. thesis, Concordia University, 2023.
41. [41] Y. Zhang, Y. Chen, H. Yu, X. Yang, R. Sun, and B. Zeng, “A Feature Adaptive Learning Method for High-Density sEMG-Based Gesture Recognition,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., Vol.5, Issue 1, Article No.44, 2021. https://doi.org/10.1145/3448114
42. [42] J. E. Lara, L. K. Cheng, O. Röhrle, and N. Paskaranandavadivel, “Muscle-Specific High-Density Electromyography Arrays for Hand Gesture Classification,” IEEE Trans. Biomed. Eng., Vol.69, Issue 5, pp. 1758-1766, 2022. https://doi.org/10.1109/TBME.2021.3131297