{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T20:04:59Z","timestamp":1773259499897,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100017577","name":"Basic Public Welfare Research Program of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ20F020016"],"award-info":[{"award-number":["LQ20F020016"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106147"],"award-info":[{"award-number":["62106147"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s13042-022-01687-4","type":"journal-article","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T03:02:46Z","timestamp":1667271766000},"page":"1119-1131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5794-8925","authenticated-orcid":false,"given":"Yinfeng","family":"Fang","sequence":"first","affiliation":[]},{"given":"Huiqiao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,1]]},"reference":[{"issue":"5","key":"1687_CR1","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.medengphy.2015.03.001","volume":"37","author":"D Joshi","year":"2015","unstructured":"Joshi D, Nakamura BH, Hahn ME (2015) High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification. Med Eng Phys 37(5):518\u2013524","journal-title":"Med Eng Phys"},{"issue":"1","key":"1687_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1743-0003-7-16","volume":"7","author":"GC Matrone","year":"2010","unstructured":"Matrone GC, Cipriani C, Secco EL, Magenes G, Carrozza MC (2010) Principal components analysis based control of a multi-DoF underactuated prosthetic hand. J Neuroeng Rehabil 7(1):1\u201313","journal-title":"J Neuroeng Rehabil"},{"key":"1687_CR3","doi-asserted-by":"crossref","unstructured":"Kuzborskij I, Gijsberts A, Caputo B.(2012) On the challenge of classifying 52 hand movements from surface electromyography. In: 2012 Annual international conference of the IEEE engineering in medicine and biology society, IEEE, p 4931\u20134937","DOI":"10.1109\/EMBC.2012.6347099"},{"key":"1687_CR4","doi-asserted-by":"publisher","first-page":"101","DOI":"10.2174\/1874120701610010101","volume":"10","author":"J Liu","year":"2016","unstructured":"Liu J, Chen W, Li M, Kang X (2016) Continuous recognition of multifunctional finger and wrist movements in amputee subjects based on sEMG and accelerometry. Open Biomed Eng J 10:101","journal-title":"Open Biomed Eng J"},{"issue":"1","key":"1687_CR5","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s13042-019-00966-x","volume":"11","author":"H Chen","year":"2020","unstructured":"Chen H, Zhang Y, Li G, Fang Y, Liu H (2020) Surface electromyography feature extraction via convolutional neural network. Int J Mach Learn Cybern 11(1):185\u2013196","journal-title":"Int J Mach Learn Cybern"},{"issue":"10","key":"1687_CR6","doi-asserted-by":"publisher","first-page":"2859","DOI":"10.1007\/s13042-021-01372-y","volume":"12","author":"RE Nogales","year":"2021","unstructured":"Nogales RE, Benalc\u00e1zar ME (2021) Hand gesture recognition using machine learning and infrared information: a systematic literature review. Int J Mach Learn Cybern 12(10):2859\u20132886","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"1687_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2014.53","volume":"1","author":"M Atzori","year":"2014","unstructured":"Atzori M, Gijsberts A, Castellini C, Caputo B, Hager A-GM, Elsig S, Giatsidis G, Bassetto F, M\u00fcller H (2014) Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci Data 1(1):1\u201313","journal-title":"Sci Data"},{"issue":"1","key":"1687_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/TNSRE.2014.2328495","volume":"23","author":"M Atzori","year":"2014","unstructured":"Atzori M, Gijsberts A, Kuzborskij I, Elsig S, Hager A-GM, Deriaz O, Castellini C, M\u00fcller H, Caputo B (2014) Characterization of a benchmark database for myoelectric movement classification. IEEE Trans Neural Syst Rehabil Eng 23(1):73\u201383","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"4","key":"1687_CR9","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1109\/TNSRE.2014.2303394","volume":"22","author":"A Gijsberts","year":"2014","unstructured":"Gijsberts A, Atzori M, Castellini C, M\u00fcller H, Caputo B (2014) Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification. IEEE Trans Neural Syst Rehabil Eng 22(4):735\u2013744","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1687_CR10","doi-asserted-by":"crossref","unstructured":"Zhou Z-H, Feng J (2019) deep forest: towards an alternative to deep neural networks. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI-17), p 3553\u20133559","DOI":"10.24963\/ijcai.2017\/497"},{"issue":"1","key":"1687_CR11","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s11517-012-0979-4","volume":"51","author":"N Jiang","year":"2013","unstructured":"Jiang N, Muceli S, Graimann B, Farina D (2013) Effect of arm position on the prediction of kinematics from EMG in amputees. Med Biol Eng Cmput 51(1):143\u2013151","journal-title":"Med Biol Eng Cmput"},{"issue":"6","key":"1687_CR12","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1109\/TNSRE.2011.2163529","volume":"19","author":"A Fougner","year":"2011","unstructured":"Fougner A, Scheme E, Chan AD, Englehart K, Stavdahl \u00d8 (2011) Resolving the limb position effect in myoelectric pattern recognition. IEEE Trans Neural Syst Rehabil Eng 19(6):644\u2013651","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"1","key":"1687_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1743-0003-9-74","volume":"9","author":"Y Geng","year":"2012","unstructured":"Geng Y, Zhou P, Li G (2012) Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees. J Neuroeng Rehabil 9(1):1\u201311","journal-title":"J Neuroeng Rehabil"},{"issue":"3","key":"1687_CR14","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TNSRE.2016.2560906","volume":"25","author":"L Liu","year":"2016","unstructured":"Liu L, Chen X, Lu Z, Cao S, Wu D, Zhang X (2016) Development of an EMG-ACC-based upper limb rehabilitation training system. IEEE Trans Neural Syst Rehabil Eng 25(3):244\u2013253","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"6","key":"1687_CR15","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TSMCA.2011.2116004","volume":"41","author":"X Zhang","year":"2011","unstructured":"Zhang X, Chen X, Li Y, Lantz V, Wang K, Yang J (2011) A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans Syst Man Cybern Part A Syst Hum 41(6):1064\u20131076","journal-title":"IEEE Trans Syst Man Cybern Part A Syst Hum"},{"key":"1687_CR16","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.eswa.2016.05.031","volume":"61","author":"RN Khushaba","year":"2016","unstructured":"Khushaba RN, Al-Timemy A, Kodagoda S, Nazarpour K (2016) Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst Appl 61:154\u2013161","journal-title":"Expert Syst Appl"},{"issue":"5","key":"1687_CR17","doi-asserted-by":"publisher","first-page":"056021","DOI":"10.1088\/1741-2560\/11\/5\/056021","volume":"11","author":"A Young","year":"2014","unstructured":"Young A, Kuiken T, Hargrove L (2014) Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses. J Neural Eng 11(5):056021","journal-title":"J Neural Eng"},{"issue":"6","key":"1687_CR18","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s00530-010-0182-0","volume":"16","author":"PK Atrey","year":"2010","unstructured":"Atrey PK, Hossain MA, El Saddik A, Kankanhalli MS (2010) Multimodal fusion for multimedia analysis: a survey. Multimedia Syst 16(6):345\u2013379","journal-title":"Multimedia Syst"},{"key":"1687_CR19","first-page":"2700","volume":"37","author":"X Xie","year":"2017","unstructured":"Xie X, Liu Z (2017) Dynamic gesture recognition method based on EMG and ACC signal. J Comput Appl 37:2700","journal-title":"J Comput Appl"},{"issue":"1","key":"1687_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12984-017-0284-4","volume":"14","author":"A Krasoulis","year":"2017","unstructured":"Krasoulis A, Kyranou I, Erden MS, Nazarpour K, Vijayakumar S (2017) Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements. J Neuroeng Rehabil 14(1):1\u201314","journal-title":"J Neuroeng Rehabil"},{"issue":"5","key":"1687_CR21","first-page":"1","volume":"19","author":"Y Guo","year":"2018","unstructured":"Guo Y, Liu S, Li Z, Shang X (2018) Bcdforest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data. BMC Bioinform 19(5):1\u201313","journal-title":"BMC Bioinform"},{"key":"1687_CR22","doi-asserted-by":"publisher","first-page":"2501","DOI":"10.1007\/s13042-020-01136-0","volume":"11","author":"P Liu","year":"2020","unstructured":"Liu P, Wang X, Yin L, Liu B (2020) Flat random forest: a new ensemble learning method towards better training efficiency and adaptive model size to deep forest. Int J Mach Learn Cybern 11:2501\u20132513","journal-title":"Int J Mach Learn Cybern"},{"key":"1687_CR23","doi-asserted-by":"publisher","first-page":"105446","DOI":"10.1016\/j.engfailanal.2021.105446","volume":"127","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Xu T, Chen C, Wang G, Zhang Z, Xiao T (2021) A hierarchical method based on improved deep forest and case-based reasoning for railway turnout fault diagnosis. Eng Fail Anal 127:105446","journal-title":"Eng Fail Anal"},{"issue":"10","key":"1687_CR24","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1109\/JBHI.2020.3019505","volume":"24","author":"L Sun","year":"2020","unstructured":"Sun L, Mo Z, Yan F, Xia L, Shan F, Ding Z, Song B, Gao W, Shao W, Shi F (2020) Adaptive feature selection guided deep forest for COVID-19 classification with chest CT. IEEE J Biomed Health Inform 24(10):2798\u20132805","journal-title":"IEEE J Biomed Health Inform"},{"key":"1687_CR25","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.is.2021.101801","volume":"101","author":"KE Daouadi","year":"2021","unstructured":"Daouadi KE, Reba\u00ef RZ, Amous I (2021) Optimizing semantic deep forest for tweet topic classification. Inf Syst 101:10\u201318","journal-title":"Inf Syst"},{"key":"1687_CR26","first-page":"107","volume":"40","author":"J Ding","year":"2021","unstructured":"Ding J, Wu Y, Luo Q, Du Y (2021) A fault diagnosis method of mechanical bearing based on the deep forest. J Vib Shock 40:107\u2013113","journal-title":"J Vib Shock"},{"key":"1687_CR27","doi-asserted-by":"publisher","first-page":"617531","DOI":"10.3389\/fnbot.2020.617531","volume":"14","author":"Y Fang","year":"2020","unstructured":"Fang Y, Yang H, Zhang X, Liu H, Tao B (2020) Multi-feature input deep forest for EEG-based emotion recognition. Front Neurorobotics 14:617531","journal-title":"Front Neurorobotics"},{"issue":"6\u20137","key":"1687_CR28","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/S1350-4533(99)00066-1","volume":"21","author":"K Englehart","year":"1999","unstructured":"Englehart K, Hudgins B, Parker PA, Stevenson M (1999) Classification of the myoelectric signal using time-frequency based representations. Med Eng Phys 21(6\u20137):431\u2013438","journal-title":"Med Eng Phys"},{"issue":"8","key":"1687_CR29","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1109\/TBME.2008.919734","volume":"55","author":"MA Oskoei","year":"2008","unstructured":"Oskoei MA, Hu H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55(8):1956\u20131965","journal-title":"IEEE Trans Biomed Eng"},{"key":"1687_CR30","doi-asserted-by":"crossref","unstructured":"Fougner A, Scheme E, Chan AD, Englehart K, Stavdahl \u00d8 (2011) A multi-modal approach for hand motion classification using surface emg and accelerometers. In: 2011 Annual international conference of the IEEE engineering in medicine and biology society, IEEE, p 4247\u20134250","DOI":"10.1109\/IEMBS.2011.6091054"},{"issue":"8","key":"1687_CR31","doi-asserted-by":"publisher","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","volume":"39","author":"A Phinyomark","year":"2012","unstructured":"Phinyomark A, Phukpattaranont P, Limsakul C (2012) Feature reduction and selection for EMG signal classification. Expert Syst Appl 39(8):7420\u20137431","journal-title":"Expert Syst Appl"},{"key":"1687_CR32","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.neucom.2021.10.104","volume":"470","author":"Y Fang","year":"2021","unstructured":"Fang Y, Yang J, Zhou D, Ju Z (2021) Modelling EMG driven wrist movements using a bio-inspired neural network. Neurocomputing 470:89\u201398","journal-title":"Neurocomputing"},{"issue":"2","key":"1687_CR33","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/JBHI.2020.2995767","volume":"25","author":"J Cheng","year":"2020","unstructured":"Cheng J, Chen M, Li C, Liu Y, Song R, Liu A, Chen X (2020) Emotion recognition from multi-channel EEG via deep forest. IEEE J Biomed Health Inf 25(2):453\u2013464","journal-title":"IEEE J Biomed Health Inf"},{"key":"1687_CR34","doi-asserted-by":"crossref","unstructured":"Yao H, He H, Wang S, Xie Z 2019) EEG-based emotion recognition using multi-scale window deep forest. In: 2019 IEEE symposium series on computational intelligence (SSCI), IEEE, p 381\u2013386 (","DOI":"10.1109\/SSCI44817.2019.9003164"},{"issue":"4","key":"1687_CR35","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.jhsa.2005.01.002","volume":"30","author":"FC Sebelius","year":"2005","unstructured":"Sebelius FC, Rosen BN, Lundborg GN (2005) Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove. J Hand Surg 30(4):780\u2013789","journal-title":"J Hand Surg"},{"key":"1687_CR36","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1613\/jair.2470","volume":"32","author":"FT Liu","year":"2008","unstructured":"Liu FT, Ting KM, Yu Y, Zhou Z-H (2008) Spectrum of variable-random trees. J Artif Intell Res 32:355\u2013384","journal-title":"J Artif Intell Res"},{"key":"1687_CR37","unstructured":"Kong D, Zhu J (2019) Gesture recognition based on fusion of surface electromyography and acceleration information. Electron Meas Technol"},{"issue":"2\u20133","key":"1687_CR38","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1504\/IJHPSA.2020.111559","volume":"9","author":"Y Zhai","year":"2020","unstructured":"Zhai Y, Lv P, Deng W, Xie X, Yu C, Gan J, Zeng J, Ying Z, Labati RD, Piuri V (2020) Facial beauty prediction via deep cascaded forest. Int J High Perform Syst Archit 9(2\u20133):97\u2013106","journal-title":"Int J High Perform Syst Archit"},{"key":"1687_CR39","doi-asserted-by":"publisher","first-page":"104036","DOI":"10.1016\/j.engappai.2020.104036","volume":"97","author":"W Liu","year":"2021","unstructured":"Liu W, Fan H, Xia M (2021) Step-wise multi-grained augmented gradient boosting decision trees for credit scoring. Eng Appl Artif Intell 97:104036","journal-title":"Eng Appl Artif Intell"},{"issue":"11","key":"1687_CR40","doi-asserted-by":"publisher","first-page":"6065","DOI":"10.1109\/JSEN.2015.2450211","volume":"15","author":"Y Fang","year":"2015","unstructured":"Fang Y, Hettiarachchi N, Zhou D, Liu H (2015) Multi-modal sensing techniques for interfacing hand prostheses: a review. IEEE Sens J 15(11):6065\u20136076","journal-title":"IEEE Sens J"},{"issue":"1","key":"1687_CR41","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s00422-008-0278-1","volume":"100","author":"C Castellini","year":"2009","unstructured":"Castellini C, Van Der Smagt P (2009) Surface EMG in advanced hand prosthetics. Biol Cybern 100(1):35\u201347","journal-title":"Biol Cybern"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01687-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01687-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01687-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T03:58:04Z","timestamp":1744171084000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01687-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,1]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["1687"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01687-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,1]]},"assertion":[{"value":"7 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}