{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:33:11Z","timestamp":1780554791818,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,6]],"date-time":"2021-02-06T00:00:00Z","timestamp":1612569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The intelligent prosthesis driven by electromyography (EMG) signal provides a solution for the movement of the disabled. The proper position of EMG sensors can improve the prosthesis\u2019s motion recognition ability. To exert the amputee\u2019s action-oriented ability and the prosthesis\u2019 control ability, the EMG spatial distribution and internal connection of the prosthetic wearer is analyzed in three kinds of movement conditions: appropriate angle, excessive angle, and angle too small. Firstly, the correlation characteristics between the EMG channels are analyzed by mutual information to construct a muscle functional network. Secondly, the network\u2019s features of different movement conditions are analyzed by calculating the characteristic of nodes and evaluating the importance of nodes. Finally, the convergent cross-mapping method is applied to construct a directed network, and the critical muscle groups which can reflect the user\u2019s movement intention are determined. Experiment shows that this method can accurately determine the EMG location and simplify the distribution of EMG sensors inside the prosthetic socket. The network characteristics of key muscle groups can distinguish different movements effectively and provide a new strategy for decoding the relationship between limb nerve control and body movement.<\/jats:p>","DOI":"10.3390\/s21041147","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"1147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Selection of EMG Sensors Based on Motion Coordinated Analysis"],"prefix":"10.3390","volume":"21","author":[{"given":"Lingling","family":"Chen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China"},{"name":"Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1747-8073","authenticated-orcid":false,"given":"Xiaotian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bokai","family":"Xuan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuojun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China"},{"name":"Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, F., Wei, X., Guo, J., Zheng, Y., Li, J., and Du, S. (August, January 29). Research Progress of Rehabilitation Exoskeletal Robot and Evaluation Methodologies Based on Bioelectrical Signals. Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Suzhou, China.","DOI":"10.1109\/CYBER46603.2019.9066492"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TNSRE.2015.2412461","article-title":"A Classification Method for User-Independent Intent Recognition for Transfemoral Amputees Using Powered Lower Limb Prostheses","volume":"24","author":"Young","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TNSRE.2019.2909585","article-title":"A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis","volume":"27","author":"Su","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/TBME.2008.2003293","article-title":"A Strategy for Identifying Locomotion Modes Using Surface Electromyography","volume":"56","author":"Huang","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/TNSRE.2019.2946625","article-title":"A Subject-Transfer Framework Based on Single-Trial EMG Analysis Using Convolutional Neural Networks","volume":"28","author":"Kim","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","unstructured":"Struijk, J.J., and Thomsen, M. (1995, January 20\u201323). Tripolar nerve cuff recording: Stimulus artifact, EMG and the recorded nerve signal. Proceedings of the 17th International Conference of the Engineering in Medicine and Biology Society, Montreal, QC, Canada."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/TNSRE.2019.2951079","article-title":"Intraoperative Responses May Predict Chronic Performance of Composite Flat Interface Nerve Electrodes on Human Femoral Nerves","volume":"27","author":"Freeberg","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1109\/TNSRE.2019.2922102","article-title":"Proportional Myoelectric Control of a Virtual Inverted Pendulum Using Residual Antagonistic Muscles: Toward Voluntary Postural Control","volume":"27","author":"Fleming","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1109\/TNSRE.2018.2805472","article-title":"EMG-Torque Dynamics Change with Contraction Bandwidth","volume":"26","author":"Golkar","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fylstra, B.L., Dai, C., Hu, X., and Huang, H.H. (2018, January 18\u201321). Characterizing Residual Muscle Properties in Lower Limb Amputees Using High Density EMG Decomposition: A Pilot Study*. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513661"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1109\/TNSRE.2020.3000735","article-title":"Design and Validation of a Lower-Limb Haptic Rehabilitation Robot","volume":"28","author":"Adamczyk","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-13-102","article-title":"A novel channel selection method for multiple motion classification using high-density electromyography","volume":"13","author":"Geng","year":"2014","journal-title":"BioMed. Eng. OnLine"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5190","DOI":"10.1016\/j.eswa.2014.03.014","article-title":"Channel and feature selection for a surface electromyographic pattern recognition task","volume":"41","author":"Mesa","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Samadani, A. (2018, January 18\u201321). EMG Channel Selection for Improved Hand Gesture Classification. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513395"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1109\/TASE.2015.2477283","article-title":"Design of a Gait Phase Recognition System That Can Cope with EMG Electrode Location Variation","volume":"14","author":"Lee","year":"2017","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Costa, \u00c1., Itkonen, M., Yamasaki, H., Alnajjar, F.S., and Shimoda, S. (2017, January 11\u201315). Importance of muscle selection for EMG signal analysis during upper limb rehabilitation of stroke patients. Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8037367"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Thiamchoo, N., and Phukpattaranont, P. (2019, January 10\u201313). The Study of EMG Channel Reduction for Hand Grasping Classification. Proceedings of the 16th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Pattaya, Chonburi, Thailand.","DOI":"10.1109\/ECTI-CON47248.2019.8955278"},{"key":"ref_18","first-page":"75","article-title":"Multiclass Recognition of Lower Limb EMG using Wavelet SVM","volume":"38","author":"She","year":"2010","journal-title":"J. Huazhong Univ. Sci. Tech. (Natl. Sci. Ed.)"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"56290","DOI":"10.1109\/ACCESS.2020.2982405","article-title":"SEMG Measurement Position and Feature Optimization Strategy for Gesture Recognition Based on ANOVA and Neural Networks","volume":"8","author":"Wu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TSMCB.2012.2185843","article-title":"An EMG-based control for an upper-limb power-assist exoskeleton robot","volume":"42","author":"Kiguchi","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part. B Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, W., Fang, Y., Zhang, G., Ju, Z., Li, G., and Liu, H. (2018, January 15\u201318). Surface Emg Channel Selection for Thumb Motion Classification signal. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), Chengdu, China.","DOI":"10.1109\/ICMLC.2018.8526988"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.medengphy.2014.04.003","article-title":"EMG feature assessment for myoelectric pattern recognition and channel selection: A study with incomplete spinal cord injury","volume":"36","author":"Liu","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/TNSRE.2007.910282","article-title":"An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface","volume":"16","author":"Huang","year":"2008","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/RBME.2016.2523799","article-title":"Treatment of the Partial Hand Amputation: An Engineering Perspective","volume":"9","author":"Imbinto","year":"2016","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1109\/TNSRE.2015.2441061","article-title":"Locomotor Adaptation by Transtibial Amputees Walking with an Experimental Powered Prosthesis under Continuous Myoelectric Control","volume":"24","author":"Huang","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s41586-019-1407-9","article-title":"Incompatible and Sterile Insect Techniques Combined Eliminate Mosquitoes","volume":"527","author":"Zheng","year":"2019","journal-title":"Nature"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective Dynamics of \u2018Small-World\u2019 Network","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1038\/nature16948","article-title":"Universal Resilience Patterns in Complex Networks","volume":"530","author":"Gao","year":"2016","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1038\/s41586-018-0872-x","article-title":"Complex Network Reveal Global Pattern of Extreme-Rainfall Teleconnection","volume":"566","author":"Boers","year":"2019","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/JBHI.2014.2342274","article-title":"Inverse Estimation of Multiple Muscle Activations from Joint Moment with Muscle Synergy Extraction","volume":"19","author":"Li","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1523\/JNEUROSCI.1759-16.2016","article-title":"Dynamic shifts in large-scale brain network balance as a function of arousal","volume":"37","author":"Young","year":"2017","journal-title":"J. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.bspc.2016.08.013","article-title":"Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series","volume":"31","author":"Yin","year":"2017","journal-title":"Biomed. Signal. Proc. Control."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Techn. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.physa.2016.05.012","article-title":"Functional brain networks in Alzheimer\u2019s disease: EEG analysis based on limited penetrable visibility graph and phase space method","volume":"460","author":"Wang","year":"2016","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_35","unstructured":"Wu, J., and Tan, Y.J. (2005, January 27\u201330). Finding the most vital node by node contraction in communication networks. Proceedings of the 2005 International Conference on Communications, Circuits and Systems, Hong Kong, China."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1038\/344734a0","article-title":"Nonlinear forecasting as away of distinguishing chaos from measurement error in time series","volume":"344","author":"Sugihara","year":"1990","journal-title":"Nature"},{"key":"ref_37","first-page":"477","article-title":"Nonlinear forecasting for the classification of natural time series","volume":"348","author":"Sugihara","year":"1994","journal-title":"Philos. Trans. Roy. Soc. A"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:20:37Z","timestamp":1760160037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,6]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041147"],"URL":"https:\/\/doi.org\/10.3390\/s21041147","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,6]]}}}