{"id":"https://openalex.org/W2209750612","doi":"https://doi.org/10.1109/eusipco.2015.7362820","title":"Combined classification and regression for simultaneous and proportional EMG control of wrist forces","display_name":"Combined classification and regression for simultaneous and proportional EMG control of wrist forces","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2209750612","doi":"https://doi.org/10.1109/eusipco.2015.7362820","mag":"2209750612"},"language":"en","primary_location":{"id":"doi:10.1109/eusipco.2015.7362820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2015.7362820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 23rd European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-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/A5014239020","display_name":"Mohammad Hossein Shahmoradi","orcid":null},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Mohammad Hossein Shahmoradi","raw_affiliation_strings":["College of Engineering, University of Tehran, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"College of Engineering, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042360234","display_name":"Mohammad Ali Akhaee","orcid":"https://orcid.org/0000-0003-3753-5618"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohammad Ali Akhaee","raw_affiliation_strings":["College of Engineering, University of Tehran, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"College of Engineering, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113562519","display_name":"Maryam S. Mirian","orcid":null},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Maryam S. Mirian","raw_affiliation_strings":["College of Engineering, University of Tehran, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"College of Engineering, University of Tehran, Tehran, Iran","institution_ids":["https://openalex.org/I23946033"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5014239020"],"corresponding_institution_ids":["https://openalex.org/I23946033"],"apc_list":null,"apc_paid":null,"fwci":0.177,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55725963,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2426","last_page":"2430"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":1.0,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9970999956130981,"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9962999820709229,"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/wrist","display_name":"Wrist","score":0.83038330078125},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5888974070549011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5747849941253662},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5426715612411499},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5403597354888916},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5085998773574829},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.48927828669548035},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4744464159011841},{"id":"https://openalex.org/keywords/degrees-of-freedom","display_name":"Degrees of freedom (physics and chemistry)","score":0.4370042383670807},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.424740731716156},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42199280858039856},{"id":"https://openalex.org/keywords/electromyography","display_name":"Electromyography","score":0.41098782420158386},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.33403146266937256},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.3222401738166809},{"id":"https://openalex.org/keywords/physical-medicine-and-rehabilitation","display_name":"Physical medicine and rehabilitation","score":0.2560122609138489},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18725329637527466},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.17331674695014954},{"id":"https://openalex.org/keywords/anatomy","display_name":"Anatomy","score":0.13229817152023315},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07686910033226013},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.0690675675868988}],"concepts":[{"id":"https://openalex.org/C2778216619","wikidata":"https://www.wikidata.org/wiki/Q185706","display_name":"Wrist","level":2,"score":0.83038330078125},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5888974070549011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5747849941253662},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5426715612411499},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5403597354888916},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5085998773574829},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.48927828669548035},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4744464159011841},{"id":"https://openalex.org/C208081375","wikidata":"https://www.wikidata.org/wiki/Q274502","display_name":"Degrees of freedom (physics and chemistry)","level":2,"score":0.4370042383670807},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.424740731716156},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42199280858039856},{"id":"https://openalex.org/C2777515770","wikidata":"https://www.wikidata.org/wiki/Q507369","display_name":"Electromyography","level":2,"score":0.41098782420158386},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.33403146266937256},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3222401738166809},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.2560122609138489},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18725329637527466},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17331674695014954},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.13229817152023315},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07686910033226013},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0690675675868988},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eusipco.2015.7362820","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2015.7362820","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 23rd European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8500000238418579,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1605883445","https://openalex.org/W1964025242","https://openalex.org/W1971702085","https://openalex.org/W1999479044","https://openalex.org/W2003803794","https://openalex.org/W2008437881","https://openalex.org/W2097092203","https://openalex.org/W2097292795","https://openalex.org/W2097690077","https://openalex.org/W2097823586","https://openalex.org/W2109743529","https://openalex.org/W2119821739","https://openalex.org/W2123167643","https://openalex.org/W2156654664","https://openalex.org/W2321409658","https://openalex.org/W2518538812","https://openalex.org/W4239510810","https://openalex.org/W6636042716","https://openalex.org/W6726633888"],"related_works":["https://openalex.org/W2406926056","https://openalex.org/W2376139493","https://openalex.org/W110334421","https://openalex.org/W2048493693","https://openalex.org/W2559360246","https://openalex.org/W1996668300","https://openalex.org/W2911808920","https://openalex.org/W2170071336","https://openalex.org/W2013246180","https://openalex.org/W2141253262"],"abstract_inverted_index":{"In":[0],"this":[1,97],"study,":[2],"a":[3,66,79],"novel":[4],"method":[5],"for":[6,25,73],"estimating":[7],"wrist":[8,35],"forces":[9],"from":[10,15,49,118],"surface":[11],"electromyogram":[12],"(EMG)":[13],"measured":[14],"the":[16,50,108,142],"upper":[17],"limb":[18],"is":[19,132],"proposed,":[20],"which":[21,131],"can":[22],"be":[23,135],"applied":[24],"unilateral":[26],"transradial":[27],"amputees.":[28],"Three":[29],"degrees":[30],"of":[31,34,110,115,141],"freedom":[32],"(DoFs)":[33],"including":[36],"flexion-extension,":[37],"abduction-adduction,":[38],"and":[39,61,76],"pronation-supination":[40],"were":[41],"used.":[42],"We":[43,93],"first":[44],"classify":[45],"feature":[46],"vectors":[47],"extracted":[48],"EMG":[51,86],"signals":[52],"into":[53],"three":[54],"classes":[55],"namely":[56],"positive":[57],"output,":[58,64],"negative":[59],"output":[60],"dead":[62],"zone":[63],"using":[65],"multiple":[67],"kernel":[68],"learning":[69],"(MKL)":[70],"algorithm.":[71],"Then":[72],"each":[74,77],"DoF":[75],"class,":[78],"neural":[80],"network":[81],"was":[82],"trained":[83],"to":[84,88,100,120,134],"associate":[85],"features":[87],"their":[89],"corresponding":[90],"force":[91,111],"outputs.":[92],"will":[94],"show":[95],"that":[96,140],"classification":[98],"prior":[99],"regression":[101],"plays":[102],"an":[103],"important":[104],"role":[105],"in":[106,127],"increasing":[107],"performance":[109],"estimation.":[112],"The":[113],"accuracy":[114],"estimation":[116],"ranges":[117],"90%":[119],"94%":[121],"(R":[122],"<sup":[123],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[124],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[125],"index)":[126],"8":[128],"able-bodied":[129],"subjects,":[130],"proved":[133],"significantly":[136],"higher":[137],"(p<;0.05)":[138],"than":[139],"previous":[143],"works.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}