{"id":"https://openalex.org/W1534552923","doi":"https://doi.org/10.1109/icaci.2015.7184763","title":"A robust gesture recognition algorithm based on surface EMG","display_name":"A robust gesture recognition algorithm based on surface EMG","publication_year":2015,"publication_date":"2015-03-01","ids":{"openalex":"https://openalex.org/W1534552923","doi":"https://doi.org/10.1109/icaci.2015.7184763","mag":"1534552923"},"language":"en","primary_location":{"id":"doi:10.1109/icaci.2015.7184763","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaci.2015.7184763","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Seventh International Conference on Advanced Computational Intelligence (ICACI)","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/A5101795150","display_name":"Ke Lin","orcid":"https://orcid.org/0009-0002-5376-7881"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Lin","raw_affiliation_strings":["Tsinghua University, Beijing, CO, China","Tsinghua University, Beijing, CO 100084 China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, CO, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua University, Beijing, CO 100084 China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111786475","display_name":"Chaohua Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaohua Wu","raw_affiliation_strings":["Tsinghua University, Beijing, CO, China","Tsinghua University, Beijing, CO 100084 China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, CO, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua University, Beijing, CO 100084 China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101768845","display_name":"Xiaoshan Huang","orcid":"https://orcid.org/0000-0002-7942-111X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoshan Huang","raw_affiliation_strings":["Tsinghua University, Beijing, CO, China","Tsinghua University, Beijing, CO 100084 China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, CO, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua University, Beijing, CO 100084 China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085617980","display_name":"Qiang Ding","orcid":"https://orcid.org/0000-0002-6226-869X"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Ding","raw_affiliation_strings":["Huawei Technologies Co.,Ltd, Beijing, CO, China","Huawei Technologies Co., Ltd, Beijing, CO 100085 China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Technologies Co.,Ltd, Beijing, CO, China","institution_ids":["https://openalex.org/I2250955327"]},{"raw_affiliation_string":"Huawei Technologies Co., Ltd, Beijing, CO 100085 China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000766901","display_name":"Xiaorong Gao","orcid":"https://orcid.org/0000-0003-0499-2740"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaorong Gao","raw_affiliation_strings":["Tsinghua University, Beijing, CO, China","Tsinghua University, Beijing, CO 100084 China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, CO, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua University, Beijing, CO 100084 China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7276,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.70530333,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"33","issue":null,"first_page":"131","last_page":"136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9991000294685364,"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":0.9991000294685364,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.9330477118492126},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7913126945495605},{"id":"https://openalex.org/keywords/gesture-recognition","display_name":"Gesture recognition","score":0.7423849105834961},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7059786319732666},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5160055160522461},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5005548000335693},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4411115050315857},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.43573009967803955},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42706042528152466},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4245629608631134},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.41461294889450073},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3678959012031555},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3582480549812317}],"concepts":[{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.9330477118492126},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7913126945495605},{"id":"https://openalex.org/C159437735","wikidata":"https://www.wikidata.org/wiki/Q1519524","display_name":"Gesture recognition","level":3,"score":0.7423849105834961},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7059786319732666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5160055160522461},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5005548000335693},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4411115050315857},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.43573009967803955},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42706042528152466},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4245629608631134},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.41461294889450073},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3678959012031555},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3582480549812317},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaci.2015.7184763","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaci.2015.7184763","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Seventh International Conference on Advanced Computational Intelligence (ICACI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W24872618","https://openalex.org/W1529072761","https://openalex.org/W1596744082","https://openalex.org/W1990512452","https://openalex.org/W2074661723","https://openalex.org/W2083742331","https://openalex.org/W2087405782","https://openalex.org/W2092102206","https://openalex.org/W2100920718","https://openalex.org/W2108225567","https://openalex.org/W2149250980","https://openalex.org/W2164092800","https://openalex.org/W2166513874","https://openalex.org/W2370902895"],"related_works":["https://openalex.org/W2902873204","https://openalex.org/W2185750513","https://openalex.org/W2028966255","https://openalex.org/W2010878661","https://openalex.org/W3147379364","https://openalex.org/W2026258298","https://openalex.org/W2466763065","https://openalex.org/W3204639664","https://openalex.org/W2970836791","https://openalex.org/W1994032303"],"abstract_inverted_index":{"This":[0],"study":[1],"researched":[2],"a":[3,38,81,126],"robust":[4],"gesture":[5,67],"recognition":[6],"algorithm":[7,13,33],"based":[8],"on":[9],"EMG.":[10],"The":[11,28,123],"proposed":[12,32],"only":[14],"needs":[15],"very":[16],"limited":[17],"training":[18,23,142],"data":[19],"(1":[20],"or":[21],"2":[22,139],"trials":[24,140],"for":[25],"each":[26],"gesture).":[27],"contribution":[29],"of":[30,55,116,125,135],"the":[31,45,53,56,61,93,101,114,117,132],"was":[34,41,72,104,110],"mainly":[35],"three-fold.":[36],"First,":[37],"shrinkage":[39],"approach":[40],"applied":[42],"to":[43,51,59,69,112],"estimate":[44],"samples'":[46],"covariance":[47],"matrix,":[48],"which":[49,83,95],"helped":[50],"improve":[52],"robustness":[54],"algorithm.":[57],"Second,":[58],"evaluate":[60,113],"system":[62,82,94,103],"performance,":[63],"classification":[64,133],"accuracy":[65,134],"and":[66],"number":[68],"be":[70],"recognized":[71],"compromised":[73],"using":[74,138],"information":[75],"transfer":[76],"rate":[77],"(ITR).":[78],"We":[79],"found":[80],"can":[84,96,144],"recognize":[85,97],"10":[86,136],"gestures":[87,137],"could":[88],"achieve":[89],"similar":[90],"ITR":[91],"as":[92,141],"20":[98],"gestures.":[99,122],"However,":[100],"10-gesture":[102],"more":[105],"robust.":[106],"Third,":[107],"K-L":[108],"divergence":[109],"used":[111],"separability":[115],"EMG":[118],"signals":[119],"from":[120],"different":[121],"result":[124],"5":[127],"subject":[128],"experiment":[129],"showed":[130],"that":[131],"set":[143],"reach":[145],"85%.":[146]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}