{"id":"https://openalex.org/W3172000794","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443400","title":"Hybrid Deep Neural Networks for Sparse Surface EMG-Based Hand Gesture Recognition","display_name":"Hybrid Deep Neural Networks for Sparse Surface EMG-Based Hand Gesture Recognition","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3172000794","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443400","mag":"3172000794"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf51394.2020.9443400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","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/A5045542279","display_name":"Elahe Rahimian","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Elahe Rahimian","raw_affiliation_strings":["Concordia University,Concordia Institute for Information Systems Engineering,Montreal,QC,Canada","Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University,Concordia Institute for Information Systems Engineering,Montreal,QC,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074844055","display_name":"Soheil Zabihi","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Soheil Zabihi","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada","Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,QC,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059155086","display_name":"Amir Asif","orcid":"https://orcid.org/0000-0002-9393-7112"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Amir Asif","raw_affiliation_strings":["York University,Department of Electrical Engineering and Computer Science,Toronto,ON,Canada","Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada"],"affiliations":[{"raw_affiliation_string":"York University,Department of Electrical Engineering and Computer Science,Toronto,ON,Canada","institution_ids":["https://openalex.org/I192455969"]},{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058253407","display_name":"Arash Mohammadi","orcid":"https://orcid.org/0000-0003-1972-7923"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arash Mohammadi","raw_affiliation_strings":["Concordia University,Concordia Institute for Information Systems Engineering,Montreal,QC,Canada","Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University,Concordia Institute for Information Systems Engineering,Montreal,QC,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5045542279"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":0.0858,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.42508154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"371","last_page":"374"},"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/T10338","display_name":"Advanced Sensor and Energy Harvesting Materials","score":0.9983999729156494,"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":0.9969000220298767,"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":0.8570155501365662},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.754224956035614},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7226866483688354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7151143550872803},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.665217936038971},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.570877194404602},{"id":"https://openalex.org/keywords/gesture-recognition","display_name":"Gesture recognition","score":0.5067067742347717},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4760272800922394},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.4354539215564728},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4239296019077301},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3305286169052124},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07997286319732666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8570155501365662},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.754224956035614},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7226866483688354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7151143550872803},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.665217936038971},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.570877194404602},{"id":"https://openalex.org/C159437735","wikidata":"https://www.wikidata.org/wiki/Q1519524","display_name":"Gesture recognition","level":3,"score":0.5067067742347717},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4760272800922394},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.4354539215564728},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4239296019077301},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3305286169052124},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07997286319732666},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/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/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf51394.2020.9443400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443400","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4399999976158142,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2123167643","https://openalex.org/W2169931829","https://openalex.org/W2516710120","https://openalex.org/W2555541061","https://openalex.org/W2807631444","https://openalex.org/W2884201223","https://openalex.org/W2887224044","https://openalex.org/W2898716605","https://openalex.org/W2911956695","https://openalex.org/W2962879438"],"related_works":["https://openalex.org/W2902873204","https://openalex.org/W2185750513","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W2010878661","https://openalex.org/W3147379364","https://openalex.org/W2026258298","https://openalex.org/W3133861977","https://openalex.org/W3204639664","https://openalex.org/W2970836791"],"abstract_inverted_index":{"The":[0,47,134,154],"paper":[1],"proposes":[2],"a":[3,44,72,111],"novel":[4],"multiple-model":[5],"(hybrid)":[6],"deep":[7,38,74],"learning":[8,39],"architecture":[9,35,49,157,185],"for":[10,59],"the":[11,27,33,53,60,86,119,123,126,131,145,162,178,183],"task":[12,61],"of":[13,30,55,62,91,125,169,196],"hand":[14,63],"gesture":[15,64],"recognition":[16],"based":[17,70,160],"on":[18,26,71,161],"multi-channel":[19],"surface":[20],"Electromyography":[21],"(sEMG)":[22],"signals.":[23],"By":[24],"capitalizing":[25],"recent":[28],"success":[29],"multiple-modeling":[31],"approaches,":[32],"proposed":[34,87,155,184],"integrates":[36],"two":[37,92,127],"models":[40,58],"simultaneously":[41,144],"and":[42,76,102,171,190],"in":[43],"parallel":[45,93],"fashion.":[46],"designed":[48],"is":[50,137,148,158],"different":[51,82],"from":[52],"majority":[54],"existing":[56],"data-driven":[57],"recognition,":[65],"which":[66],"are":[67],"commonly":[68],"developed":[69],"stand-alone":[73],"model":[75,101],"can":[77],"hardly":[78],"provide":[79],"robustness":[80],"across":[81],"scenarios.":[83],"More":[84],"specifically,":[85],"hybrid":[88,156],"solution":[89],"consists":[90],"paths,":[94],"i.e.,":[95],"one":[96,103],"Long-Short":[97],"Term":[98],"Memory":[99],"(LSTM)":[100],"Convolutional":[104],"Neural":[105],"Networks":[106],"(CNN)":[107],"path,":[108],"followed":[109],"by":[110],"fully":[112],"connected":[113],"multilayer":[114],"fusion":[115,120],"network":[116],"acting":[117],"as":[118],"centre":[121],"combining":[122],"outputs":[124],"paths":[128],"to":[129,139,150],"perform":[130],"classification":[132,194],"task.":[133],"LSTM":[135],"path":[136,147],"utilized":[138],"extract":[140,151],"temporal":[141],"features":[142],"while":[143],"CNN":[146],"used":[149],"spatial":[152],"features.":[153],"evaluated":[159],"NinaPro":[163],"DB2":[164],"dataset.":[165],"Our":[166],"comprehensive":[167],"set":[168],"experiments":[170],"comparisons":[172],"with":[173],"state-of-the-art":[174],"approaches":[175],"obtained":[176],"over":[177],"same":[179],"dataset":[180],"shows":[181],"that":[182],"significantly":[186],"outperforms":[187],"its":[188],"counterparts":[189],"achieves":[191],"exceptionally":[192],"high":[193],"performance":[195],"98.01.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
