{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T18:47:52Z","timestamp":1785178072276,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,26]],"date-time":"2020-01-26T00:00:00Z","timestamp":1579996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Priority research &amp; design program of Chongqing technology innovation and application demonstration","award":["cstc2017zdcy-zdyfX0036"],"award-info":[{"award-number":["cstc2017zdcy-zdyfX0036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.<\/jats:p>","DOI":"10.3390\/s20030672","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T07:41:11Z","timestamp":1580110871000},"page":"672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":202,"title":["Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals"],"prefix":"10.3390","volume":"20","author":[{"given":"Lin","family":"Chen","sequence":"first","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7163-1318","authenticated-orcid":false,"given":"Jianting","family":"Fu","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China"},{"name":"School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haochen","family":"Li","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.bspc.2007.07.009","article-title":"Myoelectric control systems\u2014A survey","volume":"2","author":"Oskoei","year":"2007","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_2","unstructured":"Phinyomark, A., Hirunviriya, S., Limsakul, C., and Phukpattaranont, P. (2010, January 19\u201321). Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. Proceedings of the ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Chiang Mai, Thailand."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","article-title":"Feature reduction and selection for EMG signal classification","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Khushaba, R.N., and Kodagoda, S. (2012, January 5\u20137). Electromyogram (EMG) Feature Reduction Using Mutual Components Analysis for Multifunction Prosthetic Fingers Control. Proceedings of the 2012 12th International Conference on Control Automation Robotics Vision (ICARCV), Guangzhou, China.","DOI":"10.1109\/ICARCV.2012.6485374"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/TBME.2003.813539","article-title":"A robust, real-time control scheme for multifunction myoelectric control","volume":"50","author":"Englehart","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pizzolato, S., Tagliapietra, L., Cognolato, M., Reggiani, M., M\u00fcller, H., and Atzori, M. (2017). Comparison of six electromyography acquisition setups on hand movement classification tasks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186132"},{"key":"ref_7","unstructured":"Wu, Y., Zheng, B., and Zhao, Y. (December, January 30). Dynamic Gesture Recognition Based on LSTM-CNN. Proceedings of the Chinese Automation Congress (CAC), Xi\u2019an, China."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TNSRE.2019.2896269","article-title":"Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning","volume":"27","author":"Fall","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cote-Allard, U., Fall, C.L., Campeau-Lecours, A., Gosselin, C., Laviolette, F., and Gosselin, B. (2017, January 5\u20138). Transfer learning for sEMG hand gestures recognition using convolutional neural networks. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122854"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/1743-0003-6-41","article-title":"Multi-subject\/dailylife activity EMG-based control of mechanical hands","volume":"6","author":"Castellini","year":"2009","journal-title":"Neuroeng. Rehabil."},{"key":"ref_11","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, NV, USA, 3\u20136 December 2012, Neural Information Processing Systems Foundation Inc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zia ur Rehman, M., Waris, A., Gilani, S.O., Jochumsen, M., Niazi, I.K., Jamil, M., Farina, D., and Kamavuako, E.N. (2018). Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques. Sensors, 18.","DOI":"10.3390\/s18082497"},{"key":"ref_13","unstructured":"Allard, U.C., Nougarou, F., Fall, C.L., Giguere, P., Gosselin, C., LaViolette, F., and Gosselin, B. (2016, January 9\u201314). A convolutional neural network for robotic arm guidance using sEMG based frequency-features. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/TRO.2012.2226386","article-title":"Improving Control of Dexterous Hand Prostheses Using Adaptive Learning","volume":"29","author":"Tommasi","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Patricia, N., Tommasit, T., and Caputo, B. (2014, January 24\u201328). Multi-source Adaptive Learning for Fast Control of Prosthetics Hand. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.477"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Orabona, F., Castellini, C., Caputo, B., Fiorilla, A.E., and Sandini, G. (2009, January 12\u201317). Model adaptation with least-squares SVM for adaptive hand prosthetics. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152247"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/fnbot.2016.00009","article-title":"Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands","volume":"10","author":"Atzori","year":"2016","journal-title":"Front. Neurorobotics"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"36571","DOI":"10.1038\/srep36571","article-title":"Gesture recognition by instantaneous surface EMG images","volume":"6","author":"Geng","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Du, Y., Jin, W., Wei, W., Hu, Y., and Geng, W. (2017). Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation. Sensors, 17.","DOI":"10.3390\/s17030458"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u20133). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_21","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2014.53","article-title":"Electromyography data for non-invasive naturally-controlled robotic hand prostheses","volume":"1","author":"Atzori","year":"2014","journal-title":"Sci. Data"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4832","DOI":"10.1016\/j.eswa.2013.02.023","article-title":"EMG feature evaluation for improving myoelectric pattern recognition robustness","volume":"40","author":"Phinyomark","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.jelekin.2012.06.005","article-title":"Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes","volume":"22","author":"Zhang","year":"2012","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10731","DOI":"10.1016\/j.eswa.2012.02.192","article-title":"Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals","volume":"39","author":"Khushaba","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Forsyth, D.A., Mundy, J.L., di Ges\u00fa, V., and Cipolla, R. (1999). Object Recognition with Gradient-Based Learning. Shape, Contour and Grouping in Computer Vision, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/3-540-46805-6"},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/672\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:30:29Z","timestamp":1760362229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,26]]},"references-count":27,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030672"],"URL":"https:\/\/doi.org\/10.3390\/s20030672","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,26]]}}}