{"id":"https://openalex.org/W3160379785","doi":"https://doi.org/10.1109/isr50024.2021.9419532","title":"Multi-Channel sEMG Signal Gesture Recognition Based on Improved CNN-LSTM Hybrid Models","display_name":"Multi-Channel sEMG Signal Gesture Recognition Based on Improved CNN-LSTM Hybrid Models","publication_year":2021,"publication_date":"2021-03-04","ids":{"openalex":"https://openalex.org/W3160379785","doi":"https://doi.org/10.1109/isr50024.2021.9419532","mag":"3160379785"},"language":"en","primary_location":{"id":"doi:10.1109/isr50024.2021.9419532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isr50024.2021.9419532","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101893008","display_name":"Dianchun Bai","orcid":"https://orcid.org/0000-0001-5642-9605"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dianchun Bai","raw_affiliation_strings":["Shenyang University of Technology, Shenyang, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang University of Technology, Shenyang, CHINA","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006265534","display_name":"Tie Liu","orcid":"https://orcid.org/0000-0002-6412-2432"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tie Liu","raw_affiliation_strings":["Shenyang University of Technology, Shenyang, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang University of Technology, Shenyang, CHINA","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050602414","display_name":"Xinghua Han","orcid":null},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghua Han","raw_affiliation_strings":["Shenyang University of Technology, Shenyang, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang University of Technology, Shenyang, CHINA","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445733","display_name":"Guo Chen","orcid":"https://orcid.org/0000-0002-5622-5458"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guo Chen","raw_affiliation_strings":["Shenyang University of Technology, Shenyang, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang University of Technology, Shenyang, CHINA","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076935808","display_name":"Yinlai Jiang","orcid":"https://orcid.org/0000-0002-0825-6444"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yinlai Jiang","raw_affiliation_strings":["University of Electro-Communications, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electro-Communications, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yokoi Hiroshi","orcid":null},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yokoi Hiroshi","raw_affiliation_strings":["University of Electro-Communications, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electro-Communications, Japan","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"111","last_page":"116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9980999827384949,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9926000237464905,"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/computer-science","display_name":"Computer science","score":0.825374960899353},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.677009105682373},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6094217896461487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5949758291244507},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5786834955215454},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5590072274208069},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.5368658900260925},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5308376550674438},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5026507377624512},{"id":"https://openalex.org/keywords/gesture-recognition","display_name":"Gesture recognition","score":0.49932408332824707},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4805651009082794},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47389402985572815},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4737478196620941},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.33495497703552246},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.07661879062652588}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.825374960899353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.677009105682373},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6094217896461487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5949758291244507},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5786834955215454},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5590072274208069},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.5368658900260925},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5308376550674438},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5026507377624512},{"id":"https://openalex.org/C159437735","wikidata":"https://www.wikidata.org/wiki/Q1519524","display_name":"Gesture recognition","level":3,"score":0.49932408332824707},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4805651009082794},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47389402985572815},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4737478196620941},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.33495497703552246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07661879062652588},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isr50024.2021.9419532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isr50024.2021.9419532","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2027933188","https://openalex.org/W2045193041","https://openalex.org/W2155893840","https://openalex.org/W2156914236","https://openalex.org/W2516710120","https://openalex.org/W2592340788","https://openalex.org/W2600327335","https://openalex.org/W2796589614","https://openalex.org/W2886917586","https://openalex.org/W2894349568","https://openalex.org/W2909481502","https://openalex.org/W2910830939","https://openalex.org/W2922311477","https://openalex.org/W6754918985"],"related_works":["https://openalex.org/W2902873204","https://openalex.org/W2185750513","https://openalex.org/W3147379364","https://openalex.org/W2010878661","https://openalex.org/W2026258298","https://openalex.org/W3204639664","https://openalex.org/W2970836791","https://openalex.org/W2805039731","https://openalex.org/W2989699735","https://openalex.org/W3174643231"],"abstract_inverted_index":{"Deep":[0],"learning":[1,54],"gesture":[2],"recognition":[3,24,57,80],"based":[4,40],"on":[5,41],"surface":[6,69,84],"electromyography":[7],"(sEMG)":[8],"is":[9],"playing":[10],"an":[11],"increasingly":[12],"important":[13],"role":[14],"in":[15],"prosthetic":[16],"hand":[17],"control.":[18],"In":[19],"order":[20],"to":[21,72,77],"improve":[22],"the":[23,56,65,79,93,97,107],"rate":[25,58],"of":[26,82],"multi-modal":[27,83],"EMG":[28,43,70,85,98],"signals,":[29],"this":[30],"paper":[31],"proposes":[32],"a":[33,74],"feature":[34,75],"model":[35,76],"construction":[36],"and":[37,50,59],"optimization":[38],"method":[39],"multi-channel":[42],"signal":[44,99],"amplification":[45],"unit.":[46],"And":[47],"through":[48],"CNN":[49],"LSTM":[51],"(CNN+LSTM)":[52],"deep":[53],"model,":[55],"acquisition":[60],"window":[61],"are":[62],"trained.":[63],"Use":[64],"established":[66],"time":[67],"series":[68],"image":[71],"construct":[73],"solve":[78],"problem":[81],"signal.":[86],"The":[87],"experimental":[88],"results":[89],"show":[90],"that":[91],"under":[92],"same":[94],"network":[95],"structure,":[96],"processed":[100],"by":[101],"Fast":[102],"Fourier":[103],"Transform":[104],"(FFT)":[105],"as":[106],"characteristic":[108],"value":[109],"has":[110],"better":[111],"performance.":[112]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
