{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:38:09Z","timestamp":1779385089721,"version":"3.53.1"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,13]],"date-time":"2017-06-13T00:00:00Z","timestamp":1497312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51675123"],"award-info":[{"award-number":["51675123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation for Innovative Research Groups of National Natural Science Foundation of China","award":["51521003"],"award-info":[{"award-number":["51521003"]}]},{"name":"Self-Planned Task of State Key Laboratory of Robotics and System","award":["SKLRS201603B"],"award-info":[{"award-number":["SKLRS201603B"]}]},{"DOI":"10.13039\/501100013286","name":"Research Fund for the Doctoral Program of Higher Education of China","doi-asserted-by":"publisher","award":["20132302110034"],"award-info":[{"award-number":["20132302110034"]}],"id":[{"id":"10.13039\/501100013286","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Information and Communications R&amp;D Promotion Programme","award":["142103017"],"award-info":[{"award-number":["142103017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% \u00b1 2.23%, p &lt; 0.05) and ISVC (13.38% \u00b1 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).<\/jats:p>","DOI":"10.3390\/s17061370","type":"journal-article","created":{"date-parts":[[2017,6,14]],"date-time":"2017-06-14T03:19:32Z","timestamp":1497410372000},"page":"1370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0625-0126","authenticated-orcid":false,"given":"Qi","family":"Huang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2884-3854","authenticated-orcid":false,"given":"Dapeng","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajie","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiyoshi","family":"Kotani","sequence":"additional","affiliation":[{"name":"Research Center for Advanced Science and Technology, the University of Tokyo and PRESTO\/JST, Tokyo 153-8904, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1097\/PHM.0b013e3181587f6c","article-title":"Upper-limb prosthetics: critical factors in device abandonment","volume":"86","author":"Biddiss","year":"2007","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Nurhazimah, N., Azizi, A.R.M., Shin-Ichiroh, Y., Anom, A.S., Hairi, Z., and Amri, M.S. (2016). A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors, 16.","DOI":"10.3390\/s16081304"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12431","DOI":"10.3390\/s130912431","article-title":"Surface Electromyography Signal Processing and Classification Techniques","volume":"13","author":"Chowdhury","year":"2013","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1682\/JRRD.2010.09.0177","article-title":"Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use","volume":"48","author":"Scheme","year":"2011","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/JBHI.2015.2490718","article-title":"Classification of Multiple Finger Motions during Dynamic Upper Limb Movements","volume":"21","author":"Yang","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/TBME.2011.2159216","article-title":"The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift","volume":"58","author":"Young","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TBME.2011.2177662","article-title":"Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration","volume":"59","author":"Young","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/TNSRE.2015.2417775","article-title":"High-density electromyography and motor skill learning for robust long-term control of a 7-DoF robot arm","volume":"24","author":"Ison","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1109\/TNSRE.2013.2282898","article-title":"Extracting signals robust to electrode number and shift for online simultaneous and proportional myoelectric control by factorization algorithms","volume":"22","author":"Muceli","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TNSRE.2015.2420654","article-title":"Towards zero retraining for myoelectric control based on common model component analysis","volume":"24","author":"Liu","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/TNSRE.2009.2023282","article-title":"Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms","volume":"17","author":"Sensinger","year":"2009","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1109\/TBME.2013.2296274","article-title":"Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control","volume":"61","author":"Amsuss","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MRA.2012.2229948","article-title":"Adaptive artificial limbs a real-time approach to prediction and anticipation","volume":"20","author":"Pilarski","year":"2013","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/TBME.2010.2093133","article-title":"Toward unsupervised adaptation of LDA for brain\u2013computer interfaces","volume":"58","author":"Vidaurre","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/TNSRE.2015.2401134","article-title":"Concurrent adaptation of human and machine improves simultaneous and proportional myoelectric control","volume":"23","author":"Hahne","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/BF00116895","article-title":"Incremental learning from noisy data","volume":"1","author":"Schlimmer","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1145\/2523813","article-title":"A survey on concept drift adaptation","volume":"46","author":"Gama","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/JBHI.2014.2380454","article-title":"Reduced daily recalibration of myoelectric prosthesis classifiers based on domain adaptation","volume":"20","author":"Liu","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/TNSRE.2015.2492619","article-title":"Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation","volume":"24","author":"Vidovic","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/1743-0003-10-44","article-title":"Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control","volume":"10","author":"Chen","year":"2013","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TRA.2003.808873","article-title":"A human-assisting manipulator teleoperated by EMG signals and arm motions","volume":"19","author":"Fukuda","year":"2003","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_24","first-page":"217","article-title":"Development of a Novel Post-Processing Algorithm for Myoelectric Pattern Classification (in Japanese)","volume":"53","author":"Kasuya","year":"2015","journal-title":"Trans. Jpn. Soc. Med. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1109\/TBME.2011.2113182","article-title":"Selective classification for improved robustness of myoelectric control under non-ideal conditions","volume":"58","author":"Scheme","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TBME.2006.889192","article-title":"A comparison of surface and intramuscular myoelectric signal classification","volume":"54","author":"Hargrove","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, D., Jiang, L., Liu, R., and Liu, H. (2013, January 12\u201314). Adaptive learning of multi-finger motion recognition based on support vector machine. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Shenzhen, China.","DOI":"10.1109\/ROBIO.2013.6739801"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.medengphy.2015.02.005","article-title":"Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control","volume":"37","author":"Liu","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). Constructing Learning Algorithms. The Nature of Statistical Learning Theory, Springer. [1st ed.].","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/JBHI.2013.2256920","article-title":"Boosting-based EMG patterns classification scheme for robustness enhancement","volume":"17","author":"Li","year":"2013","journal-title":"IEEE J. Biomed. Health Infor."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/JBHI.2013.2249590","article-title":"Classification of finger movements for the dexterous hand prosthesis control with surface electromyography","volume":"17","author":"Bugmann","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Benatti, S., Milosevic, B., Farella, E., Gruppioni, E., and Benini, L. (2017). A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies. Sensors, 17.","DOI":"10.3390\/s17040869"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Syed, N.A., Liu, H., and Sung, K.K. (1999, January 15\u201318). Handling concept drifts in incremental learning with support vector machines. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD\u201999), San Diego, CA, USA.","DOI":"10.1145\/312129.312267"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/B:MACH.0000008082.80494.e0","article-title":"Benchmarking least squares support vector machine classifiers","volume":"54","author":"Gestel","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1007\/s00500-009-0434-0","article-title":"Adaptive pruning algorithm for least squares support vector machine classifier","volume":"14","author":"Yang","year":"2010","journal-title":"Soft Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1177\/1468087411420280","article-title":"Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction","volume":"13","author":"Wong","year":"2012","journal-title":"Int. J. Engine Res."},{"key":"ref_37","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":"2013","journal-title":"IEEE Trans. Robot."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (2006). Transductive inference and semi-supervised learning. Semi-Supervised Learning, MIT Press. [1st ed.].","DOI":"10.7551\/mitpress\/6173.003.0032"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chapelle, O., Sch\u00f6lkopf, B., and Zien, A. (2006). Introduction to semi-supervised learning. Semi-Supervised Learning, MIT Press. [1st ed.].","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"861","DOI":"10.3233\/IDA-2009-0397","article-title":"On the window size for classification in changing environments","volume":"13","author":"Kuncheva","year":"2009","journal-title":"Intell. Data Anal."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"281","DOI":"10.3233\/IDA-2004-8305","article-title":"Learning drifting concepts: Example selection vs. example weighting","volume":"8","author":"Klinkenberg","year":"2004","journal-title":"Intell. Data Anal."},{"key":"ref_42","unstructured":"Kuh, A., Petsche, T., and Rivest, R.L. (1990, January 26\u201329). Learning time-varying concepts. Proceedings of the Advances in Neural Information Processing Systems 1990 (NIPS-3), Denver, CO, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1162\/089976603321891855","article-title":"Asymptotic behaviors of support vector machines with Gaussian kernel","volume":"15","author":"Keerthi","year":"2003","journal-title":"Neural Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chang, C.C., and Lin, C.J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2.","DOI":"10.1145\/1961189.1961199"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","article-title":"A comparison of methods for multiclass support vector machines","volume":"13","author":"Hsu","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neucom.2013.10.038","article-title":"Sparse least square support vector machine via coupled compressive pruning","volume":"131","author":"Yang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1016\/j.neunet.2004.07.002","article-title":"Fast exact leave-one-out cross-validation of sparse least-squares support vector machines","volume":"17","author":"Cawley","year":"2004","journal-title":"Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Golub, G.H., and Loan, C.F. (2013). Cholesky updating and downdating. Matrix Computations, The John Hopkins University Press. [4th ed.].","DOI":"10.56021\/9781421407944"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100","DOI":"10.2307\/2346830","article-title":"Algorithm AS 136: A k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"Appl. Stat."},{"key":"ref_50","unstructured":"(2010). 13E200 Myobock Electrode, Otto Bock Healthcare Products GmbH."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Atzori, M., Gijsberts, A., Heynen, S., and Hager, A.G.M. (2012, January 24\u201327). Building the Ninapro database: A resource for the biorobotics community. Proceedings of the IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, Roma, Italy.","DOI":"10.1109\/BioRob.2012.6290287"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.jphysparis.2009.08.008","article-title":"Fine detection of grasp force and posture by amputees via surface electromyography","volume":"103","author":"Castellini","year":"2009","journal-title":"J. Physiol. Paris"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/70.34763","article-title":"On grasp choice, grasp models, and the design of hands for manufacturing tasks","volume":"5","author":"Cutkosky","year":"1989","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1250007","DOI":"10.1142\/S0219843612500077","article-title":"Dynamic hand motion recognition based on transient and steady-state EMG signals","volume":"9","author":"Yang","year":"2012","journal-title":"Int. J. Humanoid Robot."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:38:55Z","timestamp":1760207935000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,13]]},"references-count":54,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["s17061370"],"URL":"https:\/\/doi.org\/10.3390\/s17061370","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,13]]}}}