{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:46:57Z","timestamp":1774597617759,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cPioneer\u201d R&amp;D Program of Zhejiang","award":["2025C01088"],"award-info":[{"award-number":["2025C01088"]}]},{"name":"\u201cPioneer\u201d R&amp;D Program of Zhejiang","award":["Y202456686"],"award-info":[{"award-number":["Y202456686"]}]},{"name":"\u201cPioneer\u201d R&amp;D Program of Zhejiang","award":["25222260-D"],"award-info":[{"award-number":["25222260-D"]}]},{"name":"\u201cPioneer\u201d R&amp;D Program of Zhejiang","award":["23222218-Y"],"award-info":[{"award-number":["23222218-Y"]}]},{"name":"Foundation of the Zhejiang Educational Committee","award":["2025C01088"],"award-info":[{"award-number":["2025C01088"]}]},{"name":"Foundation of the Zhejiang Educational Committee","award":["Y202456686"],"award-info":[{"award-number":["Y202456686"]}]},{"name":"Foundation of the Zhejiang Educational Committee","award":["25222260-D"],"award-info":[{"award-number":["25222260-D"]}]},{"name":"Foundation of the Zhejiang Educational Committee","award":["23222218-Y"],"award-info":[{"award-number":["23222218-Y"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["2025C01088"],"award-info":[{"award-number":["2025C01088"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["Y202456686"],"award-info":[{"award-number":["Y202456686"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["25222260-D"],"award-info":[{"award-number":["25222260-D"]}]},{"name":"Natural Science Foundation of Zhejiang Province","award":["23222218-Y"],"award-info":[{"award-number":["23222218-Y"]}]},{"name":"Foundation of Zhejiang Sci-Tech University","award":["2025C01088"],"award-info":[{"award-number":["2025C01088"]}]},{"name":"Foundation of Zhejiang Sci-Tech University","award":["Y202456686"],"award-info":[{"award-number":["Y202456686"]}]},{"name":"Foundation of Zhejiang Sci-Tech University","award":["25222260-D"],"award-info":[{"award-number":["25222260-D"]}]},{"name":"Foundation of Zhejiang Sci-Tech University","award":["23222218-Y"],"award-info":[{"award-number":["23222218-Y"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Electromyography (EMG) signals reflect hand motion intention and exhibit a certain degree of amplitude symmetry. Nowadays, recognition of hand motion intention based on EMG has enriched its burgeoning promotion in various applications, such as rehabilitation, prostheses, and intelligent supply chains. For instance, the motion intentions of humans can be conveyed to logistics equipment, thereby improving the level of intelligence in a supply chain. To enhance the recognition accuracy of multiple hand motion intentions, this paper proposes a hand motion intention recognition method that decodes EMG signals based on improved long short-term memory (LSTM). Firstly, we performed preprocessing and utilized overlapping sliding windows on EMG segments. Secondly, we chose LSTM and improved it so as to capture features and enable prediction of hand motion intention. Specifically, we introduced the optimal key hyperparameter combination in the LSTM model using a genetic algorithm (GA). We found that our proposed method achieved relatively high accuracy in detecting hand motion intention, with average accuracies of 92.0% (five gestures) and 89.7% (seven gestures), while the highest accuracy reached 100.0% (seven gestures). Our paper may provide a way to predict the motion intention of the human hand for intention communication.<\/jats:p>","DOI":"10.3390\/sym17101587","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T10:27:53Z","timestamp":1758623273000},"page":"1587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Hand Motion Intention Recognition Method That Decodes EMG Signals Based on an Improved LSTM"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8797-0709","authenticated-orcid":false,"given":"Tian-Ao","family":"Cao","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"},{"name":"Weihai Sunfull Electronics Group Co., Ltd., Weihai 264200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7427-0702","authenticated-orcid":false,"given":"Hongyou","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0723-9477","authenticated-orcid":false,"given":"Zhengkui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwei","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengze","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4870-9361","authenticated-orcid":false,"given":"Lurong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8466-2404","authenticated-orcid":false,"given":"Yanyun","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6209-6605","authenticated-orcid":false,"given":"Jijun","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China"},{"name":"Provincial Key Laboratory for Research and Translation of Kidney Deficiency-Stasis-Turbidity Disease, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"140053","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_2","doi-asserted-by":"crossref","unstructured":"Vogel, J., Hagengruber, A., Iskandar, M., and Quere, G. (2020\u201324, January 24). EDAN: An EMG-controlled daily assistant to help people with physical disabilities. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341156"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/JBHI.2020.3027303","article-title":"A home-based bilateral rehabilitation system with sEMG-based real-time variable stiffness","volume":"25","author":"Liu","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1109\/TNSRE.2013.2290017","article-title":"Complexity analysis of EMG signals for patients after stroke during robot-aided rehabilitation training using fuzzy approximate entropy","volume":"22","author":"Sun","year":"2013","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"38850","DOI":"10.1109\/ACCESS.2023.3267674","article-title":"Artificial intelligence for sEMG-based muscular movement recognition for hand prosthesis","volume":"11","author":"Hye","year":"2023","journal-title":"IEEE Access"},{"key":"ref_6","first-page":"650","article-title":"Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees","volume":"24","author":"Khushaba","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TCPMT.2018.2799987","article-title":"An sEMG-based human\u2013robot interface for robotic hands using machine learning and synergies","volume":"8","author":"Meattini","year":"2018","journal-title":"IEEE Trans. Compon. Pack. Manuf. Technol."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R., Turner, J., and Landay, J.A. (2009, January 4\u20137). Enabling always-available input with muscle-computer interfaces. Proceedings of the 22nd Annual ACM Symposium on User Interface Software and Technology, Victoria, Canada.","DOI":"10.1145\/1622176.1622208"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TNSRE.2014.2305111","article-title":"The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges","volume":"22","author":"Farina","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TNSRE.2022.3210258","article-title":"A VR-based motor imagery training system with EMG-based real-time feedback for post-stroke rehabilitation","volume":"31","author":"Lin","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jo, H.N., Park, S.W., Choi, H.G., Han, S.H., and Kim, T.S. (2022). Development of an Electrooculogram (EOG) and surface Electromyogram (sEMG)-based human computer interface (HCI) using a bone conduction headphone integrated bio-signal acquisition system. Electronics, 11.","DOI":"10.3390\/electronics11162561"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qing, Z., Lu, Z., Cai, Y., and Wang, J. (2021). Elements influencing sEMG-based gesture decoding: Muscle fatigue, forearm angle and acquisition time. Sensors, 21.","DOI":"10.3390\/s21227713"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.bbe.2021.03.004","article-title":"Hand movement recognition from sEMG signals using Fourier decomposition method","volume":"41","author":"Fatimah","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5246","DOI":"10.1109\/JSEN.2023.3344700","article-title":"Enhancing gesture classification using active EMG band and advanced feature extraction technique","volume":"24","author":"Rani","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Junior, J.J.A.M., Freitas, M.L.B., Siqueira, H.V., Lazzaretti, A.E., Pichorim, S.F., and Stevan, S.L. (2020). Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomed. Signal Process. Control, 59.","DOI":"10.1016\/j.bspc.2020.101920"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"118282","DOI":"10.1016\/j.eswa.2022.118282","article-title":"sEMG time\u2013frequency features for hand movements classification","volume":"210","author":"Karheily","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shen, S., Gu, K., Chen, X., and Wang, R. (2019, January 24\u201325). Motion classification based on sEMG signals using deep learning. Proceedings of the Machine Learning and Intelligent Communications: 4th International Conference (MLICOM 2019), Nanjing, China.","DOI":"10.1007\/978-3-030-32388-2_48"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"157","DOI":"10.32604\/cmes.2022.020035","article-title":"A novel SE-CNN attention architecture for sEMG-based hand gesture recognition","volume":"134","author":"Xu","year":"2023","journal-title":"CMES"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Luo, X., Huang, W., Wang, Z., Li, Y., and Duan, X. (2024). InRes-ACNet: Gesture recognition model of multi-scale attention mechanisms based on surface Electromyography signals. Appl. Sci., 14.","DOI":"10.3390\/app14083237"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sehat, K., Shokouhyan, S.M., Abdallah, N.K., and Khalafet, K. (2023). Deep network optimization using a genetic algorithm for recognizing hand gestures via EMG signals. Preprints, 2023010075.","DOI":"10.20944\/preprints202301.0075.v1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6979","DOI":"10.1109\/JIOT.2020.2979328","article-title":"Learning effective spatial\u2013temporal features for sEMG armband-based gesture recognition","volume":"7","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102869","DOI":"10.1016\/j.jelekin.2024.102869","article-title":"A fast gradient convolution kernel compensation method for surface electromyogram decomposition","volume":"76","author":"Lin","year":"2024","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/TSMC.1975.5408479","article-title":"Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes","volume":"SMC-5","author":"Graupe","year":"1975","journal-title":"IEEE Trans. Syst. Man Cybern.-Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.medengphy.2005.10.016","article-title":"Toward direct biocontrol using surface EMG signals: Control of finger and wrist joint models","volume":"29","author":"Reddy","year":"2007","journal-title":"Med. Eng. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.bspc.2016.11.018","article-title":"Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm","volume":"33","author":"Ghaemi","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","first-page":"1","article-title":"Learning k for kNN classification","volume":"8","author":"Zhang","year":"2017","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kim, S., and Lee, S.P. (2023). A BiLSTM\u2013Transformer and 2D CNN architecture for emotion recognition from speech. Electronics, 12.","DOI":"10.3390\/electronics12194034"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"34691","DOI":"10.1007\/s11042-023-16997-0","article-title":"A hybrid framework for time series trends: Embedding social network\u2019s sentiments and optimized stacked LSTM using evolutionary algorithm","volume":"83","author":"Kumar","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Khademi, Z., Ebrahimi, F., and Kordy, H.M. (2022). A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals. Comput. Biol. Med., 143.","DOI":"10.1016\/j.compbiomed.2022.105288"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cao, K., Zhang, T., and Huang, J. (2024). Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-55483-x"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_34","unstructured":"Jozefowicz, R., Zaremba, W., and Sutskever, I. (2015, January 6\u201311). An empirical exploration of recurrent network architectures. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1587\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:47:35Z","timestamp":1760035655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1587"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["sym17101587"],"URL":"https:\/\/doi.org\/10.3390\/sym17101587","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]}}}