{"id":"https://openalex.org/W3134435969","doi":"https://doi.org/10.1109/access.2021.3065105","title":"Deep Neural Network\u2013Based Double-Check Method for Fall Detection Using IMU-L Sensor and RGB Camera Data","display_name":"Deep Neural Network\u2013Based Double-Check Method for Fall Detection Using IMU-L Sensor and RGB Camera Data","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3134435969","doi":"https://doi.org/10.1109/access.2021.3065105","mag":"3134435969"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3065105","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3065105","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374404.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374404.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059663795","display_name":"Deok-Won Lee","orcid":"https://orcid.org/0000-0001-8787-5608"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Deok-Won Lee","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-8787-5608","affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088028367","display_name":"Kooksung Jun","orcid":"https://orcid.org/0000-0002-8757-2014"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kooksung Jun","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-8757-2014","affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072791681","display_name":"Khawar Naheem","orcid":"https://orcid.org/0000-0003-0820-3569"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Khawar Naheem","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084294297","display_name":"Mun Sang Kim","orcid":"https://orcid.org/0000-0002-6050-6594"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Mun Sang Kim","raw_affiliation_strings":["School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea","institution_ids":["https://openalex.org/I39534123"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39534123"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.8139,"has_fulltext":true,"cited_by_count":42,"citation_normalized_percentile":{"value":0.92092363,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"48064","last_page":"48079"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9930999875068665,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.8853609561920166},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8163420557975769},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.745197057723999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7261647582054138},{"id":"https://openalex.org/keywords/gyroscope","display_name":"Gyroscope","score":0.6875681281089783},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6198037266731262},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.5815820693969727},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5238859057426453},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44992247223854065},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1470295786857605}],"concepts":[{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.8853609561920166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8163420557975769},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.745197057723999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7261647582054138},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.6875681281089783},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6198037266731262},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.5815820693969727},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5238859057426453},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44992247223854065},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1470295786857605},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3065105","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3065105","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374404.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:429e448c14ab46619f2e769f2df51da2","is_oa":true,"landing_page_url":"https://doaj.org/article/429e448c14ab46619f2e769f2df51da2","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 48064-48079 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3065105","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3065105","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374404.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5452688907","display_name":null,"funder_award_id":"20003762","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"},{"id":"https://openalex.org/G7860297130","display_name":null,"funder_award_id":"10063300","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"}],"funders":[{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3134435969.pdf","grobid_xml":"https://content.openalex.org/works/W3134435969.grobid-xml"},"referenced_works_count":91,"referenced_works":["https://openalex.org/W65469549","https://openalex.org/W163964114","https://openalex.org/W169531684","https://openalex.org/W215027519","https://openalex.org/W639708223","https://openalex.org/W1512655439","https://openalex.org/W1578208389","https://openalex.org/W1593727536","https://openalex.org/W1904625144","https://openalex.org/W1924770834","https://openalex.org/W1932624639","https://openalex.org/W1971422403","https://openalex.org/W1991069594","https://openalex.org/W2002416791","https://openalex.org/W2009790822","https://openalex.org/W2010545956","https://openalex.org/W2012227652","https://openalex.org/W2020676607","https://openalex.org/W2028456138","https://openalex.org/W2043495858","https://openalex.org/W2054342195","https://openalex.org/W2059584741","https://openalex.org/W2064675550","https://openalex.org/W2068232426","https://openalex.org/W2074099390","https://openalex.org/W2089937542","https://openalex.org/W2100716893","https://openalex.org/W2103413038","https://openalex.org/W2107878631","https://openalex.org/W2114868488","https://openalex.org/W2119126533","https://openalex.org/W2120661106","https://openalex.org/W2157331557","https://openalex.org/W2166786963","https://openalex.org/W2183685922","https://openalex.org/W2193464378","https://openalex.org/W2211074723","https://openalex.org/W2292230603","https://openalex.org/W2293580120","https://openalex.org/W2293634267","https://openalex.org/W2314677348","https://openalex.org/W2319308093","https://openalex.org/W2413337736","https://openalex.org/W2419341522","https://openalex.org/W2511127197","https://openalex.org/W2516506354","https://openalex.org/W2587019100","https://openalex.org/W2594864412","https://openalex.org/W2602315639","https://openalex.org/W2605300166","https://openalex.org/W2613718673","https://openalex.org/W2620635248","https://openalex.org/W2724727449","https://openalex.org/W2766863773","https://openalex.org/W2791500836","https://openalex.org/W2801475069","https://openalex.org/W2802084776","https://openalex.org/W2896783937","https://openalex.org/W2901136733","https://openalex.org/W2914868535","https://openalex.org/W2917442576","https://openalex.org/W2921943530","https://openalex.org/W2940682483","https://openalex.org/W2941398656","https://openalex.org/W2944777698","https://openalex.org/W2963610939","https://openalex.org/W2964241181","https://openalex.org/W2964242908","https://openalex.org/W2973255917","https://openalex.org/W2979421026","https://openalex.org/W3000884041","https://openalex.org/W3008308086","https://openalex.org/W3011632920","https://openalex.org/W3038075422","https://openalex.org/W3042983365","https://openalex.org/W3043900635","https://openalex.org/W3045295985","https://openalex.org/W3047828507","https://openalex.org/W3100380967","https://openalex.org/W3106884574","https://openalex.org/W3118673420","https://openalex.org/W3146185530","https://openalex.org/W6620707391","https://openalex.org/W6639936665","https://openalex.org/W6640212811","https://openalex.org/W6678082130","https://openalex.org/W6684257448","https://openalex.org/W6696934422","https://openalex.org/W6725990282","https://openalex.org/W6733081445","https://openalex.org/W6756486208"],"related_works":["https://openalex.org/W2356006086","https://openalex.org/W2545168295","https://openalex.org/W1973973903","https://openalex.org/W2365897603","https://openalex.org/W4234814094","https://openalex.org/W2156308897","https://openalex.org/W3195533899","https://openalex.org/W3208523813","https://openalex.org/W4287084017","https://openalex.org/W3179745820"],"abstract_inverted_index":{"Existing":[0],"methods":[1,217],"for":[2,40],"fall":[3,9,21,41,95,123,166,179,206],"detection":[4,42],"may":[5,14],"not":[6,23],"detect":[7,32,118],"a":[8,16,20,37,54,64,85,121,145,150,164,178,186,226,234,238],"when":[10,19],"it":[11],"occurs":[12],"or":[13],"generate":[15],"false":[17],"alarm":[18],"does":[22],"occur.":[24],"In":[25],"order":[26],"to":[27,117,157,171,193,198,229],"overcome":[28],"these":[29],"limitations":[30],"and":[31,53,71,73,92,104,175,200,240],"falls":[33,212],"with":[34,131],"100%":[35,242],"accuracy,":[36],"double-check":[38],"method":[39,89,210,232],"in":[43,237,244],"elderly":[44,98],"people":[45],"via":[46,100],"an":[47,67,74,97,105,221],"inertial":[48],"measurement":[49],"unit-location":[50],"(IMU-L)":[51],"sensor":[52,62,69,81,103,111],"red-green-blue":[55],"(RGB)":[56],"camera":[57],"is":[58,63,82,112,129,155,191],"proposed.":[59],"The":[60,87,109],"IMU-L":[61,102,110],"combination":[65],"of":[66,96,138,142],"IMU":[68,143,162,222],"(accelerometer":[70],"gyroscope)":[72],"ultrawideband":[75],"signal-based":[76],"location":[77,127,174],"sensor;":[78],"the":[79,94,101,115,125,132,139,161,168,172,183,194,202],"RGB":[80,106,195],"mounted":[83],"on":[84,114],"robot.":[86],"proposed":[88],"involves":[90],"detecting":[91],"confirming":[93],"individual":[99],"image,":[107],"respectively.":[108],"worn":[113],"body":[116],"falls.":[119,159],"When":[120,160],"potential":[122],"occurs,":[124],"individual's":[126],"information":[128],"synchronized":[130],"motion":[133],"data.":[134],"During":[135,182],"detection,":[136],"because":[137],"sequential":[140],"nature":[141],"data,":[144],"deep":[146],"learning":[147],"technique":[148,190],"called":[149],"recurrent":[151],"neural":[152,188],"network":[153],"(RNN)":[154],"trained":[156],"classify":[158],"indicates":[163],"suspected":[165],"situation,":[167],"robot":[169],"moves":[170],"corresponding":[173],"confirms":[176],"whether":[177],"has":[180],"occurred.":[181],"confirmation":[184],"stage,":[185],"convolutional":[187],"network-based":[189],"applied":[192],"image":[196],"data":[197],"recognize":[199],"confirm":[201],"fall.":[203],"Repeated":[204],"confirmed":[205],"detections":[207],"using":[208,233],"this":[209],"classified":[211],"more":[213],"accurately":[214],"than":[215],"existing":[216],"that":[218],"use":[219],"only":[220],"sensor.":[223],"We":[224],"conducted":[225],"real-time":[227],"experiment":[228],"validate":[230],"our":[231,245],"dataset":[235],"developed":[236],"laboratory":[239],"achieved":[241],"accuracy":[243],"experimental":[246],"environment.":[247]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2021-03-15T00:00:00"}
