{"id":"https://openalex.org/W2477004689","doi":"https://doi.org/10.1109/tie.2016.2593693","title":"Recognizing the Gradual Changes in sEMG Characteristics Based on Incremental Learning of Wavelet Neural Network Ensemble","display_name":"Recognizing the Gradual Changes in sEMG Characteristics Based on Incremental Learning of Wavelet Neural Network Ensemble","publication_year":2016,"publication_date":"2016-07-21","ids":{"openalex":"https://openalex.org/W2477004689","doi":"https://doi.org/10.1109/tie.2016.2593693","mag":"2477004689"},"language":"en","primary_location":{"id":"doi:10.1109/tie.2016.2593693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2016.2593693","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Electronics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015883801","display_name":"Feng Duan","orcid":"https://orcid.org/0000-0002-2179-2460"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Duan","raw_affiliation_strings":["College of Computer and Control Engineering, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Control Engineering, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012356951","display_name":"Lili Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Dai","raw_affiliation_strings":["College of Computer and Control Engineering, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer and Control Engineering, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":2.1344,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.86710983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"64","issue":"5","first_page":"4276","last_page":"4286"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9757999777793884,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.665597140789032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.647104024887085},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6355999708175659},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5953797698020935},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental learning","score":0.545081377029419},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4447326958179474},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.43204164505004883},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.42017948627471924},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4185648560523987},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32652390003204346}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.665597140789032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.647104024887085},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6355999708175659},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5953797698020935},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.545081377029419},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4447326958179474},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.43204164505004883},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.42017948627471924},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4185648560523987},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32652390003204346},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tie.2016.2593693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tie.2016.2593693","pdf_url":null,"source":{"id":"https://openalex.org/S58031724","display_name":"IEEE Transactions on Industrial Electronics","issn_l":"0278-0046","issn":["0278-0046","1557-9948"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W64908097","https://openalex.org/W138870297","https://openalex.org/W435157458","https://openalex.org/W1145659577","https://openalex.org/W1587019244","https://openalex.org/W1764484653","https://openalex.org/W1965628919","https://openalex.org/W1966455806","https://openalex.org/W1977872363","https://openalex.org/W2013427553","https://openalex.org/W2024034573","https://openalex.org/W2040871222","https://openalex.org/W2048285753","https://openalex.org/W2065630015","https://openalex.org/W2066519883","https://openalex.org/W2068649373","https://openalex.org/W2076671199","https://openalex.org/W2096330959","https://openalex.org/W2099419573","https://openalex.org/W2104664243","https://openalex.org/W2108695617","https://openalex.org/W2117680746","https://openalex.org/W2119071749","https://openalex.org/W2122920178","https://openalex.org/W2130867619","https://openalex.org/W2156394514","https://openalex.org/W2160512933","https://openalex.org/W2162319643","https://openalex.org/W2164006146","https://openalex.org/W2171219007","https://openalex.org/W2190312883","https://openalex.org/W2195092867","https://openalex.org/W2295398032","https://openalex.org/W2343814947","https://openalex.org/W2398214544","https://openalex.org/W4241336979","https://openalex.org/W6712332468"],"related_works":["https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W2784060934","https://openalex.org/W2902714807","https://openalex.org/W2537489131","https://openalex.org/W2394084632","https://openalex.org/W2358293514","https://openalex.org/W2046633342","https://openalex.org/W2059273319","https://openalex.org/W2077021924"],"abstract_inverted_index":{"Most":[0],"myoelectric":[1,199],"prosthetic":[2,200],"hands":[3],"use":[4],"a":[5,42,87,96,179],"fixed":[6,31,107],"pattern":[7,32,108],"recognition":[8,33,109],"model":[9,34],"to":[10,28,49,51,76,174],"identify":[11],"the":[12,30,52,64,81,84,106,111,125,128,132,141,145,157,165,168,186,191],"user's":[13],"hand":[14,37,93],"motion":[15,38],"commands.":[16],"Since":[17],"surface":[18],"electromyogram":[19],"(sEMG)":[20],"characteristics":[21,162],"vary":[22],"with":[23,105,118,131,144],"time,":[24],"it":[25],"is":[26,172,189],"difficult":[27],"employ":[29],"in":[35,55,95,160,198],"identifying":[36],"commands":[39],"stably":[40],"for":[41,99],"long":[43,180],"period":[44,181],"of":[45,83,89,115,127,140,182],"time.":[46,183],"In":[47,123],"order":[48],"adapt":[50],"gradual":[53,158],"changes":[54,159],"sEMG":[56,161],"characteristics,":[57],"we":[58],"utilized":[59],"incremental":[60,116],"learning":[61,74,117],"based":[62],"on":[63],"wavelet":[65],"neural":[66],"network":[67],"(WNN)":[68],"ensemble,":[69],"and":[70],"used":[71],"negative":[72],"correlation":[73],"(NCL)":[75],"train":[77],"it.":[78],"To":[79],"verify":[80],"effect":[82],"proposed":[85,166,192],"method,":[86,167],"group":[88],"subjects":[90],"executed":[91],"six":[92],"motions":[94],"continual":[97],"experiment":[98],"more":[100,136],"than":[101,138],"2":[102],"h.":[103],"Compared":[104],"model,":[110],"classification":[112],"accuracy":[113,170],"rate":[114,171],"nonintegration":[119],"becomes":[120],"substantially":[121],"improved.":[122],"addition,":[124],"results":[126,150],"WNN":[129,142],"ensemble":[130,143],"fixed-size":[133],"mode":[134],"are":[135],"stable":[137],"those":[139],"growth":[146],"mode.":[147],"The":[148],"experimental":[149],"demonstrate":[151],"that":[152],"our":[153],"method":[154,193],"can":[155,194],"recognize":[156],"stably.":[163],"Using":[164],"average":[169],"found":[173],"be":[175,195],"92.17%,":[176],"even":[177],"after":[178],"Moreover,":[184],"since":[185],"update":[187],"time":[188],"short,":[190],"successfully":[196],"applied":[197],"hands.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}