CAM-MR-MS based gesture recognition method using sEMG

Abstract

With the continuous concern for the disabled and the elderly, intelligent prosthetics and service robots have been widely applied. This paper provides a method for gesture recognition using forearm surface electromyography (sEMG), including an adaptive channel selection method to simplify the sEMG measurement. Based on the forearm muscle groups corresponding to different movements, surface skin areas are divided, and the Myo bracelet is used to collect sEMG signals from these areas. A method combined with channel attention module, multi-channel relationship feature extraction module and multi-scale skip connection module is built to adaptively select the signals from certain skin areas and recognize the seven gestures during experiment. The comparative experimental results indicate that this method can adaptively extract the optimal channel combination and show effective recognition results. It improved the practicability for the sEMG-based gesture recognition.

Keywords

Surface skin area / surface electromyography (sEMG) / channel selection / gesture recognition / optimal channel combination

References

[1] Sabariego C,Lee L.Generating comprehensive functioning and disability data worldwide: development process, data analyses strategy and reliability of the WHO and World Bank Model Disability Survey.Arch Public Health2022;80:6 PMCID:PMC8725367

[2] Ersen M,Sariel S.Cognition-enabled robot manipulation in human environments: requirements, recent work, and open problems.IEEE Robot Automat Mag2017;24:108-22

[3] Sun Y,Li G.Intelligent human computer interaction based on non redundant EMG signal.Alex Eng J2020;59:1149-57

[4] Igual C,Hahne JM.Myoelectric control for upper limb prostheses.Electronics2019;8:1244

[5] Mane SM,Kazi FS.Hand motion recognition from single channel surface EMG using wavelet & artificial neural network.Procedia Comput Sci2015;49:58-65

[6] Qu Y,Teng S.Reduce sEMG channels for hand gesture recognition. In: 2020 3rd IEEE International Conference on Information Communication and Signal Processing (ICICSP), Shanghai, China. Sep 12-15, 2020. IEEE, 2020. pp. 215-20.

[7] Ma L,Li Z,Xu Z.An optimal channel selection method for EMG signals based on gradient boosting decision tree.Inf Control2020;49:114-21

[8] Wang Z,Li G.Facilitate sEMG-Based human–machine interaction through channel optimization.Int J Human Robot2019;16:1941001

[9] Huang H,Li G.An analysis of EMG electrode configuration for targeted muscle reinnervation based neural machine interface.IEEE Trans Neural Syst Rehabil Eng2008;16:37-45 PMCID:PMC4316368

[10] Liu J,Li G.EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury.Med Eng Phys2014;36:975-80 PMCID:PMC4043864

[11] Niu Y,Zeng H,Xiong B.Optimizing sEMG gesture recognition: leveraging channel selection and feature compression for improved accuracy and computational efficiency.Appl Sci2024;14:3389

[12] Zhang Z,Zhao S.Real-time surface EMG pattern recognition for hand gestures based on support vector machine. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China. Dec 06-08, 2019. IEEE, 2019; pp. 1258-62.

[13] Lee KH,Byun S.Electromyogram-based classification of hand and finger gestures using artificial neural networks.Sensors2021;22:225 PMCID:PMC8749583

[14] Geng W,Jin W,Hu Y.Gesture recognition by instantaneous surface EMG images.Sci Rep2016;6:36571 PMCID:PMC5109222

[15] Zhang J,Li S.Human movements classification using multi-channel surface EMG signals and deep learning technique. In: 2019 International Conference on Cyberworlds (CW), Kyoto, Japan. Oct 02-04, 2019. IEEE, 2019; pp. 267-73.

[16] Hu Y,Wei W,Kankanhalli M.A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.PLoS One2018;13:e0206049 PMCID:PMC6207326

[17] Wang L,Zheng B.Research on sEMG based gesture recognition usingthe Atention-based LSTM-CNN with stationary wavclet packet transform. In: 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), Suzhou, China. Apr 22-24, 2022. IEEE, 2022; p. 1-6.

[18] Alzahrani M,Althunayan T,Almehaid F.Functional anatomy of the hand: prevalence of the linburg–Comstock anomaly in a young saudi population.J Musculoskelet Surg Res2018;2:21

[19] Boles CA,Cardwell AB.The forearm: anatomy of muscle compartments and nerves.AJR Am J Roentgenol2000;174:151-9

[20] Liu X,Wang J.Gesture recognition of continuous wavelet transform and deep convolution attention network.Math Biosci Eng2023;20:11139-54

[21] Tang D,He Y.Strain-insensitive elastic surface electromyographic (sEMG) electrode for efficient recognition of exercise intensities.Micromachines2020;11:239 PMCID:PMC7143104

[22] Gijsberts A,Castellini C,Caputo B.Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification.IEEE Trans Neural Syst Rehabil Eng2014;22:735-44

[23] Wang M,Peng C,Fan Z.Research on EMG segmentation algorithm and walking analysis based on signal envelope and integral electrical signal.Photon Netw Commun2019;37:195-203

[24] Khan TI,Ide S.Analysis of aging effect on lower limb muscle activity using short time Fourier transform and wavelet decomposition of electromyography signal.AIP Adv2023;13:055011

[25] Kundu AS,Lenka PK.Hand gesture recognition based omnidirectional wheelchair control using IMU and EMG sensors.J Intell Robot Syst2018;91:529-41

[26] Wang Q,Zhu P.ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA. Jun 13-19, 2020. IEEE, 2020. pp. 11534-42.

[27] Woo S,Lee J. CBAM: convolutional block attention module. arXiv 2018, arXiv:1807.16521. Available online: https://doi.org/10.48550/arXiv.1807.06521. [accessed on 1 Apr 2025]

[28] Xu X.A hybrid model based on ResNet and GCN for sEMG-based gesture recognition.J Beijing Inst Technol2023;32:219-29

[29] Xu Z,Xiang W.A novel SE-CNN attention architecture for sEMG-based hand gesture recognition.Comput Model Eng Sci2023;134:157-77

[30] Zhang Y,Fan Q,Li X.Research on sEMG-based gesture recognition by dual-view deep learning.IEEE Access2022;10:32928-37

[31] Minu MS,Subashka Ramesh SS.Optimal squeeze net with deep neural network-based arial image classification model in unmanned aerial vehicles.TS2022;39:275-81

[32] Sardeli AV,Mori MA,Chacon-Mikahil MPT.Resistance training prevents muscle loss induced by caloric restriction in obese elderly individuals: a systematic review and meta-analysis.Nutrients2018;10:423 PMCID:PMC5946208

[33] Bramley JL,Bader DL.Changes in tissue composition and load response after transtibial amputation indicate biomechanical adaptation.Ann Biomed Eng2021;49:3176-88 PMCID:PMC8671271