{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T14:15:19Z","timestamp":1780755319858,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2012,1,30]],"date-time":"2012-01-30T00:00:00Z","timestamp":1327881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high.<\/jats:p>","DOI":"10.3390\/s120201130","type":"journal-article","created":{"date-parts":[[2012,1,30]],"date-time":"2012-01-30T11:10:39Z","timestamp":1327921839000},"page":"1130-1147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor"],"prefix":"10.3390","volume":"12","author":[{"given":"Xueyan","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunhui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congyi","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2012,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Merletti, R., and Parker, P. (2004). Electromyography: Physiology, Engineering and Noninvasive Applications, John Wiley & Sons, Inc.","DOI":"10.1002\/0471678384"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TNSRE.2011.2108667","article-title":"Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees","volume":"19","author":"Cipriani","year":"2001","journal-title":"IEEE Trans. Neural Syst. Reh. En"},{"key":"ref_3","unstructured":"Jin, D.W., Zhang, R.H., Zhang, J.C., Wang, R.C., and Gruver, W.A. (2000, January 8\u201311). An Intelligent Above-Knee Prosthsis with EMG-Based Terrain Identification. Nashville, TN, USA."},{"key":"ref_4","unstructured":"Ito, K., Tsuji, T., Kato, A., and Ito, M. EMG Pattern Classification for a Prosthetic Forearm with Three Degrees of Freedom. Tokyo, Japan."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/10.387202","article-title":"Performance of Above Elbow Body-Powered Prostheses in Visually Guided Unconstrained Motion Tasks","volume":"42","author":"Doringer","year":"1995","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/TBME.1982.324954","article-title":"EMG Pattern Analysis and Classification for a Prosthetic Arm","volume":"29","author":"Saridis","year":"1982","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"ref_7","first-page":"255","article-title":"Fine Detection of Grasp Force and Posture by Amputees via Surface Electromyography","volume":"103","author":"Castellini","year":"2009","journal-title":"Neurorobotics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s00422-008-0278-1","article-title":"Surface EMG in Advanced Hand Prosthetics","volume":"100","author":"Castellini","year":"2008","journal-title":"Biol. Cybern"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1682\/JRRD.2010.08.0161","article-title":"Myoelectric Forearm Prostheses: State of the Art from a User-Centered Perspective","volume":"48","author":"Peerdeman","year":"2011","journal-title":"J. Rehabil. Res. Dev"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/TBME.2010.2068298","article-title":"Simultaneous and Proportional Force Estimation for Multifunction Myoelectric Prostheses Using Mirrored Bilateral Training","volume":"58","author":"Nielsen","year":"2011","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/10.204774","article-title":"A New Strategy for Multifunction Myoelectric Control","volume":"40","author":"Hudgins","year":"1993","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/70.538982","article-title":"Myoelectric Teleoperation of a Complex Robotic Hand","volume":"12","author":"Farry","year":"1996","journal-title":"IEEE Trans. Robotics Automat"},{"key":"ref_13","unstructured":"Fukuda, O., Tsuji, T., and Kaneko, M. (October, January 29). An EMG Controlled Robotics Manipulator using Neural Networks. Sendai, Japan."},{"key":"ref_14","unstructured":"Kuribayashi, K., Okimura, K., and Taniguchi, T. (1993, January 26\u201330). A Discrimination System using Neural Network for EMG-Controller Prostheses\u2014Integral Type of EMG Signal Processing. Tokyo, Japan."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D., Morris, D., and Balakrishnan, R. (2008, January 5\u201310). Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces. Florence, Italy.","DOI":"10.1145\/1357054.1357138"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4283","DOI":"10.1016\/j.eswa.2009.11.072","article-title":"Portable Hand Motion Classifier for Multi-Channel Surface Electromyography Recognition using Grey Relational Analysis","volume":"37","author":"Du","year":"2010","journal-title":"Expert Syst. Appl"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1615\/CritRevBiomedEng.v30.i456.80","article-title":"Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal","volume":"30","author":"Zecca","year":"2002","journal-title":"Crit. Rev. Biomed. Eng"},{"key":"ref_18","first-page":"1773","article-title":"Short Time Fourier Analysis of the Electromyogram: Fast Movements and Constant Contraction","volume":"33","author":"Hannaford","year":"1986","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"ref_19","unstructured":"Du, S.J., and Vuskovic, M. (2004, January 8\u201310). Temporal vs. Spectral Approach to Feature Extraction from Prehensile EMG Signals. Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S1050-6411(02)00071-8","article-title":"Crosstalk in Surface Electromyography of the Proximal Forearm during Gripping Tasks","volume":"13","author":"Mogk","year":"2003","journal-title":"J. Electromyograph. Kinesiol"},{"key":"ref_21","unstructured":"Haykin, S. (1999). Neural Networks: A Comprehensive Foundation, Prentice Hall."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Micheli-Tzanakou, E. (2000). Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence, CRC Press.","DOI":"10.1201\/9781420049770"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S1350-4533(99)00055-7","article-title":"A Hybrid Approach for EMG Pattern Analysis for Classification of Arm Movements using Statistical and Fuzzy Techniques","volume":"21","author":"Micera","year":"1999","journal-title":"Med. Eng. Phys"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/91.366565","article-title":"A. Method for Fuzzy Rule Extraction Directly from Numerical Data and Its Application to Pattern Classification","volume":"3","author":"Abe","year":"1995","journal-title":"IEEE Trans. Fuzzy Syst"},{"key":"ref_25","unstructured":"Tang, X.Y., Liu, Y.H, Lu, C.Y., and Poon, W.L. (2011). Biologically Inspired Robotics, CRC Press Taylor & Francis Group."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A Concordance Correlation Coefficient to Evaluate Reproducibility","volume":"45","author":"Lin","year":"1989","journal-title":"Biometrics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1006\/nimg.1999.0472","article-title":"Plurality and Resemblance in fMRI Data Analysis","volume":"10","author":"Lange","year":"1999","journal-title":"Neuroimage"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Webb, A.R. (2002). Statistical Pattern Recognition, John Wiley & Sons, Inc.","DOI":"10.1002\/0470854774"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/2\/1130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:48:38Z","timestamp":1760219318000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/2\/1130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,1,30]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2012,2]]}},"alternative-id":["s120201130"],"URL":"https:\/\/doi.org\/10.3390\/s120201130","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,1,30]]}}}