{"id":"https://openalex.org/W2909645133","doi":"https://doi.org/10.1109/jbhi.2018.2808281","title":"Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data","display_name":"Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data","publication_year":2018,"publication_date":"2018-02-20","ids":{"openalex":"https://openalex.org/W2909645133","doi":"https://doi.org/10.1109/jbhi.2018.2808281","mag":"2909645133","pmid":"https://pubmed.ncbi.nlm.nih.gov/29994460"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2018.2808281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2018.2808281","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100778313","display_name":"Na L\u00fc","orcid":"https://orcid.org/0000-0002-7436-2691"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Na Lu","raw_affiliation_strings":["Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011523154","display_name":"Yidan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yidan Wu","raw_affiliation_strings":["Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072183477","display_name":"Feng Li","orcid":"https://orcid.org/0000-0001-6957-5388"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Feng","raw_affiliation_strings":["Second Medical Imaging Department, Affiliated Hospital of Xi'an Jiaotong University (Ninth Hospital of Xi'an), Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Second Medical Imaging Department, Affiliated Hospital of Xi'an Jiaotong University (Ninth Hospital of Xi'an), Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076764967","display_name":"Jinbo Song","orcid":"https://orcid.org/0000-0003-2779-953X"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinbo Song","raw_affiliation_strings":["Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":16.4229,"has_fulltext":false,"cited_by_count":337,"citation_normalized_percentile":{"value":0.99438562,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"23","issue":"1","first_page":"314","last_page":"323"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9991999864578247,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9991999864578247,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9988999962806702,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9937999844551086,"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/computer-science","display_name":"Computer science","score":0.8303979635238647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7790501117706299},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7278813719749451},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6269319653511047},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5404293537139893},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5120726227760315},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5042592287063599},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48701196908950806},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.4529598355293274},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4159581661224365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8303979635238647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7790501117706299},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7278813719749451},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6269319653511047},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5404293537139893},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5120726227760315},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5042592287063599},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48701196908950806},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.4529598355293274},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4159581661224365},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000058","descriptor_name":"Accidental Falls","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000058","descriptor_name":"Accidental Falls","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000058","descriptor_name":"Accidental Falls","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001696","descriptor_name":"Biomechanical Phenomena","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D001696","descriptor_name":"Biomechanical Phenomena","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D001696","descriptor_name":"Biomechanical Phenomena","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006802","descriptor_name":"Human Activities","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":false},{"descriptor_ui":"D006802","descriptor_name":"Human Activities","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":false},{"descriptor_ui":"D006802","descriptor_name":"Human Activities","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014741","descriptor_name":"Video Recording","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014741","descriptor_name":"Video Recording","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014741","descriptor_name":"Video Recording","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2018.2808281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2018.2808281","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:29994460","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29994460","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1768697346","display_name":null,"funder_award_id":"20110491662","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G5235391020","display_name":null,"funder_award_id":"2012T50805","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G830021807","display_name":null,"funder_award_id":"61673312","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8711908385","display_name":null,"funder_award_id":"2016IRS19","funder_id":"https://openalex.org/F4320335791","funder_display_name":"Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335791","display_name":"Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W215027519","https://openalex.org/W1514535095","https://openalex.org/W1522301498","https://openalex.org/W1522734439","https://openalex.org/W1566752807","https://openalex.org/W1686810756","https://openalex.org/W1842967820","https://openalex.org/W1964812476","https://openalex.org/W1977324509","https://openalex.org/W1983364832","https://openalex.org/W1986387010","https://openalex.org/W2011847543","https://openalex.org/W2016053056","https://openalex.org/W2022508996","https://openalex.org/W2023756500","https://openalex.org/W2028191869","https://openalex.org/W2028661866","https://openalex.org/W2056818943","https://openalex.org/W2064675550","https://openalex.org/W2072396104","https://openalex.org/W2074099390","https://openalex.org/W2076068958","https://openalex.org/W2086622679","https://openalex.org/W2089947415","https://openalex.org/W2095705004","https://openalex.org/W2096300942","https://openalex.org/W2097117768","https://openalex.org/W2100495367","https://openalex.org/W2100716893","https://openalex.org/W2108598243","https://openalex.org/W2115343816","https://openalex.org/W2126579184","https://openalex.org/W2131889717","https://openalex.org/W2133564696","https://openalex.org/W2152449912","https://openalex.org/W2152599926","https://openalex.org/W2155326828","https://openalex.org/W2160815625","https://openalex.org/W2161565164","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2164086816","https://openalex.org/W2183182206","https://openalex.org/W2194775991","https://openalex.org/W2209990155","https://openalex.org/W2235034809","https://openalex.org/W2274865900","https://openalex.org/W2292929658","https://openalex.org/W2508429489","https://openalex.org/W2618253408","https://openalex.org/W2618530766","https://openalex.org/W2750647644","https://openalex.org/W2772973030","https://openalex.org/W2919115771","https://openalex.org/W2962835968","https://openalex.org/W2963246338","https://openalex.org/W2963446712","https://openalex.org/W2963731876","https://openalex.org/W2963750390","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W3151123641","https://openalex.org/W4251262448","https://openalex.org/W4294557331","https://openalex.org/W6630875275","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6674330103","https://openalex.org/W6679434410","https://openalex.org/W6684983439","https://openalex.org/W6696861079"],"related_works":["https://openalex.org/W2789522126","https://openalex.org/W2066693961","https://openalex.org/W2368363778","https://openalex.org/W122584421","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Fall":[0],"detection":[1,9,26,98,209],"is":[2,27,60,78,99,146,171,181,189],"an":[3,109],"important":[4,147],"public":[5],"healthcare":[6],"problem.":[7],"Timely":[8],"could":[10,114,128,138],"enable":[11],"instant":[12],"delivery":[13],"of":[14,122,156],"medical":[15],"service":[16],"to":[17,44,51,62,72,80,107,183,194],"the":[18,36,55,74,116,134,154,185,204],"injured.":[19],"A":[20],"popular":[21],"nonintrusive":[22],"solution":[23],"for":[24,96,118,148],"fall":[25,64,97,120,149,179,199,208],"based":[28,94,166],"on":[29,207,222],"videos":[30],"obtained":[31,221],"through":[32],"ambient":[33],"camera,":[34],"and":[35,48,66,113,133],"corresponding":[37],"methods":[38],"usually":[39],"require":[40],"a":[41,46,87,161,196],"large":[42,119],"dataset":[43,121,174],"train":[45,108,184,195],"classifier":[47,197],"are":[49,70],"inclined":[50],"be":[52],"influenced":[53],"by":[54],"image":[56],"quality.":[57],"However,":[58],"it":[59],"hard":[61],"collect":[63],"data":[65,106],"instead":[67],"simulated":[68],"falls":[69],"recorded":[71],"construct":[73],"training":[75],"dataset,":[76],"which":[77,101,145,188],"restricted":[79],"limited":[81],"quantity.":[82],"To":[83,151],"address":[84],"these":[85],"problems,":[86],"three-dimensional":[88],"convolutional":[89],"neural":[90],"network":[91],"(3-D":[92],"CNN)":[93],"method":[95],"developed,":[100],"only":[102,129],"uses":[103],"video":[104],"kinematic":[105],"automatic":[110],"feature":[111,141],"extractor":[112],"circumvent":[115],"requirement":[117],"deep":[123],"learning":[124],"solution.":[125],"2-D":[126],"CNN":[127],"encode":[130],"spatial":[131,167],"information,":[132],"employed":[135,182],"3-D":[136,186],"convolution":[137],"extract":[139],"motion":[140],"from":[142],"temporal":[143],"sequence,":[144],"detection.":[150],"further":[152],"locate":[153],"region":[155],"interest":[157],"in":[158],"each":[159],"frame,":[160],"long":[162],"short-term":[163],"memory":[164],"(LSTM)":[165],"visual":[168],"attention":[169],"scheme":[170,206],"incorporated.":[172],"Sports":[173],"Sports-1":[175],"M":[176],"with":[177,192,198,211],"no":[178],"examples":[180],"CNN,":[187],"then":[190],"combined":[191],"LSTM":[193],"dataset.":[200],"Experiments":[201],"have":[202],"verified":[203],"proposed":[205],"benchmark":[210],"high":[212],"accuracy":[213],"as":[214],"100%.":[215],"Superior":[216],"performance":[217],"has":[218],"also":[219],"been":[220],"other":[223],"activity":[224],"databases.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":32},{"year":2024,"cited_by_count":42},{"year":2023,"cited_by_count":43},{"year":2022,"cited_by_count":76},{"year":2021,"cited_by_count":65},{"year":2020,"cited_by_count":41},{"year":2019,"cited_by_count":25},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}