{"id":"https://openalex.org/W3112158255","doi":"https://doi.org/10.1109/smc42975.2020.9283422","title":"Deep-Learning LSTM Mechanism and Wearable Devices based Virtual Fitness-Coach Information System for Barbell Bench Press","display_name":"Deep-Learning LSTM Mechanism and Wearable Devices based Virtual Fitness-Coach Information System for Barbell Bench Press","publication_year":2020,"publication_date":"2020-10-11","ids":{"openalex":"https://openalex.org/W3112158255","doi":"https://doi.org/10.1109/smc42975.2020.9283422","mag":"3112158255"},"language":"en","primary_location":{"id":"doi:10.1109/smc42975.2020.9283422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9283422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5101108269","display_name":"Chun-Chieh Hsiao","orcid":null},"institutions":[{"id":"https://openalex.org/I50519452","display_name":"Lunghwa University of Science and Technology","ror":"https://ror.org/001y2wd07","country_code":"TW","type":"education","lineage":["https://openalex.org/I50519452"]},{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Chun-Chieh Hsiao","raw_affiliation_strings":["Lunghwa University of Science and Technology, Taoyuan, Taiwan","National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Lunghwa University of Science and Technology, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I50519452"]},{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060142360","display_name":"Po\u2010Chieh Yu","orcid":"https://orcid.org/0000-0001-8894-0854"},"institutions":[{"id":"https://openalex.org/I50519452","display_name":"Lunghwa University of Science and Technology","ror":"https://ror.org/001y2wd07","country_code":"TW","type":"education","lineage":["https://openalex.org/I50519452"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Po-Chieh Yu","raw_affiliation_strings":["Lunghwa University of Science and Technology, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Lunghwa University of Science and Technology, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I50519452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056595228","display_name":"Ren-Guey Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I118292597","display_name":"National Taipei University of Technology","ror":"https://ror.org/00cn92c09","country_code":"TW","type":"education","lineage":["https://openalex.org/I118292597"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ren-Guey Lee","raw_affiliation_strings":["National Taipei University of Technology, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taipei University of Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I118292597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102022248","display_name":"Haiyan Jiang","orcid":"https://orcid.org/0000-0002-4099-480X"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]},{"id":"https://openalex.org/I129708740","display_name":"Fujian Medical University","ror":"https://ror.org/050s6ns64","country_code":"CN","type":"education","lineage":["https://openalex.org/I129708740"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyan Jiang","raw_affiliation_strings":["Electrical Engineering and Automation, Fuzhou University, Fuzhou, China","Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering and Automation, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China","institution_ids":["https://openalex.org/I129708740"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101108269"],"corresponding_institution_ids":["https://openalex.org/I16733864","https://openalex.org/I50519452"],"apc_list":null,"apc_paid":null,"fwci":0.0858,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.4133514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3880","last_page":"3885"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9797000288963318,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9797000288963318,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9764000177383423,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10352","display_name":"Physical Activity and Health","score":0.9337999820709229,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.6939677596092224},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.6871278882026672},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6865919232368469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6017857789993286},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5633880496025085},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.5071516036987305},{"id":"https://openalex.org/keywords/bench-press","display_name":"Bench press","score":0.5050716996192932},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4866950809955597},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.45648810267448425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44582098722457886},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4223673641681671},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3404383063316345},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.12599405646324158}],"concepts":[{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.6939677596092224},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6871278882026672},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6865919232368469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6017857789993286},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5633880496025085},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.5071516036987305},{"id":"https://openalex.org/C2777188771","wikidata":"https://www.wikidata.org/wiki/Q14093","display_name":"Bench press","level":3,"score":0.5050716996192932},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4866950809955597},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.45648810267448425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44582098722457886},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4223673641681671},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3404383063316345},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.12599405646324158},{"id":"https://openalex.org/C3019424981","wikidata":"https://www.wikidata.org/wiki/Q12834857","display_name":"Resistance training","level":2,"score":0.0},{"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/C1862650","wikidata":"https://www.wikidata.org/wiki/Q186005","display_name":"Physical therapy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc42975.2020.9283422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9283422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309618","display_name":"Ministry of Science and Technology","ror":"https://ror.org/02b207r52"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W78161199","https://openalex.org/W1414062319","https://openalex.org/W1534268656","https://openalex.org/W2010590614","https://openalex.org/W2020514077","https://openalex.org/W2024063229","https://openalex.org/W2062533874","https://openalex.org/W2099761293","https://openalex.org/W2101401380","https://openalex.org/W2160257086","https://openalex.org/W2160594359","https://openalex.org/W2161842525","https://openalex.org/W2571184881","https://openalex.org/W4239167631","https://openalex.org/W4251186568","https://openalex.org/W4298300677","https://openalex.org/W6631904614","https://openalex.org/W7074122107"],"related_works":["https://openalex.org/W4387490204","https://openalex.org/W4386414453","https://openalex.org/W4388937883","https://openalex.org/W4389848424","https://openalex.org/W4293205612","https://openalex.org/W4352976590","https://openalex.org/W4401011972","https://openalex.org/W4385349203","https://openalex.org/W4297839701","https://openalex.org/W3200170908"],"abstract_inverted_index":{"This":[0],"study":[1],"aims":[2],"to":[3,41,68,87,119,167,229,253,263,270,280,310,319,327,339,344,352,355,360],"design":[4,42],"and":[5,25,36,49,53,74,108,112,136,194,205,213,276,283,330,359,388,411,423],"develop":[6],"a":[7,30],"virtual":[8],"fitness-coach":[9],"information":[10],"system":[11,402],"for":[12,93,153,286,379,419,431],"barbell":[13,61,97],"bench":[14,63,99],"press":[15,64],"based":[16],"on":[17],"deep-learning":[18],"Long":[19,162],"Short":[20,163],"Term":[21,164],"Memory":[22,165],"(LSTM)":[23,166],"mechanism":[24],"wearable":[26,45,91,390,421,429],"devices.":[27,46,391],"We":[28,216,341],"utilizes":[29],"set":[31,191],"of":[32,56,78,125,139,233,244,258,274,367,383,427,433],"three-axis":[33],"accelerometers,":[34],"gyroscopes":[35,277],"Electromyography":[37],"(EMG)":[38],"sensing":[39],"modules":[40],"our":[43,89,368,389,399,420,428],"proposed":[44,90],"Through":[47],"computer":[48],"smartphone,":[50],"the":[51,57,76,102,104,113,120,129,144,149,154,161,169,178,183,186,195,223,231,234,236,241,245,255,265,272,289,303,306,322,331,346,365,384,404],"analysis":[52,269],"real-time":[54],"assessment":[55],"weight":[58,72,94,170],"training":[59,73,79,95,159,171,256,347],"in":[60,71,96,182,219,302,403],"free":[62,98],"can":[65],"be":[66],"performed":[67],"avoid":[69],"injury":[70],"improve":[75],"quality":[77],"performance.In":[80],"this":[81],"study,":[82],"21":[83],"subjects":[84,378],"are":[85,110,116,207,225,325],"recruited":[86,376],"use":[88,264],"devices":[92,422,430],"press.":[100],"In":[101],"training,":[103],"subject's":[105,114],"physiological":[106],"signals":[107,115],"videos":[109],"captured,":[111],"extracted":[117,145],"according":[118],"11":[121],"most":[122],"common":[123,266],"kinds":[124,138],"errors":[126,135],"marked":[127],"by":[128],"fitness":[130],"instructor,":[131],"including":[132,292],"7":[133],"posture":[134],"4":[137],"muscle":[140],"force":[141],"errors.":[142,172],"After":[143],"signal":[146],"is":[147,151,238,248,294,298,308,317],"normalized,":[148],"data":[150,273,348],"fed":[152],"Recurrent":[155],"Neural":[156],"Network":[157],"(RNN)":[158],"through":[160],"classify":[168],"The":[173,392,413],"experimental":[174],"results":[175],"show":[176,394],"that":[177,218],"classification":[179,184,188],"threshold":[180],"used":[181],"has":[185,336],"best":[187],"result":[189],"when":[190,305,315],"at":[192],"0.5,":[193],"overall":[196,242],"average":[197],"accuracy,":[198,199],"recall":[200],"rate,":[201],"F1":[202],"Score,":[203],"FPR":[204],"FNR":[206],"91.84%,":[208],"89.25%,":[209],"88.17%,":[210],"88.18%,":[211],"6.50%":[212],"11.83%,":[214],"respectively.":[215],"found":[217],"some":[220],"categories,":[221],"because":[222],"sensors":[224],"not":[226,299],"powerful":[227],"enough":[228],"capture":[230],"characteristics":[232],"errors,":[235],"accuracy":[237,243,304,358],"low.":[239],"While":[240],"other":[246],"categories":[247],"higher":[249],"than":[250],"85%.In":[251],"order":[252],"accelerate":[254,361],"speed":[257],"LSTM,":[259],"we":[260,373],"also":[261,415],"try":[262],"factor":[267],"extraction":[268],"reduce":[271,345],"accelerometers":[275],"from":[278,349],"24":[279,311],"18,":[281],"12":[282,320],"6":[284],"dimensions":[285,351,354],"training.":[287],"When":[288],"total":[290],"dimension":[291,307],"EMG":[293],"30":[295,350],"dimensions,":[296,321],"there":[297],"much":[300],"difference":[301],"reduced":[309,318,326],"or":[312],"18.":[313],"However":[314],"it":[316],"evaluation":[323],"metrics":[324],"below":[328],"70%,":[329],"False":[332],"Negative":[333],"Rate":[334],"(FNR)":[335],"risen":[337],"sharply":[338],"30.21%.":[340],"therefore":[342],"choose":[343],"18":[353],"maintain":[356],"recognition":[357],"LSTM":[362],"training.To":[363],"verify":[364],"feasibility":[366],"Virtual":[369],"Fitness-Coach":[370],"Information":[371],"System,":[372],"have":[374],"further":[375,425],"5":[377],"user":[380],"satisfaction":[381],"survey":[382],"instant":[385,400],"voice":[386],"feedback":[387,401],"users":[393,414],"relatively":[395,417],"high":[396],"satisfiaction":[397],"about":[398],"following":[405],"aspects:":[406],"helpfulness,":[407],"clearance,":[408],"reliability,":[409],"correctness,":[410],"performance.":[412],"feel":[416],"comfortable":[418],"suggest":[424],"simplification":[426],"ease":[432],"wearing.":[434]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
