{"id":"https://openalex.org/W4287219797","doi":"https://doi.org/10.3390/s22155482","title":"A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition","display_name":"A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition","publication_year":2022,"publication_date":"2022-07-22","ids":{"openalex":"https://openalex.org/W4287219797","doi":"https://doi.org/10.3390/s22155482","pmid":"https://pubmed.ncbi.nlm.nih.gov/35897985"},"language":"en","primary_location":{"id":"doi:10.3390/s22155482","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22155482","pdf_url":"https://www.mdpi.com/1424-8220/22/15/5482/pdf?version=1658497587","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/15/5482/pdf?version=1658497587","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052546112","display_name":"Zhenxiao Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenxiao Zhao","raw_affiliation_strings":["School of Information Science and Technology, Fudan University, Shanghai 200433, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Fudan University, Shanghai 200433, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037124262","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0003-3744-9701"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai 200433, China"],"raw_orcid":"https://orcid.org/0000-0003-3744-9701","affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai 200433, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110301590","display_name":"Huiliang Shang","orcid":"https://orcid.org/0000-0002-9553-1589"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huiliang Shang","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai 200433, China","School of Information Science and Technology, Fudan University, Shanghai 200433, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai 200433, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Information Science and Technology, Fudan University, Shanghai 200433, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110301590"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":1.0161,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.76937532,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"22","issue":"15","first_page":"5482","last_page":"5482"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9927999973297119,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9717000126838684,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6646184921264648},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.62872713804245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6091593503952026},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5951890349388123},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5331477522850037},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4684116244316101},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.44818025827407837},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.43228384852409363},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41658663749694824},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3956485092639923},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3386155962944031},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2753673195838928},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17323875427246094},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11549520492553711}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6646184921264648},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.62872713804245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6091593503952026},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5951890349388123},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5331477522850037},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4684116244316101},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.44818025827407837},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.43228384852409363},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41658663749694824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3956485092639923},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3386155962944031},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2753673195838928},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17323875427246094},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11549520492553711},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[{"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":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","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":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012863","descriptor_name":"Skeleton","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012863","descriptor_name":"Skeleton","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012863","descriptor_name":"Skeleton","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22155482","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22155482","pdf_url":"https://www.mdpi.com/1424-8220/22/15/5482/pdf?version=1658497587","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:35897985","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35897985","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":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:64741a11b45045fb8b6f69cd090a08b2","is_oa":true,"landing_page_url":"https://doaj.org/article/64741a11b45045fb8b6f69cd090a08b2","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":"Sensors, Vol 22, Iss 15, p 5482 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/15/5482/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22155482","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 15; Pages: 5482","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9332296","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9332296","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22155482","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22155482","pdf_url":"https://www.mdpi.com/1424-8220/22/15/5482/pdf?version=1658497587","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5799999833106995,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4287219797.pdf","grobid_xml":"https://content.openalex.org/works/W4287219797.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W402210544","https://openalex.org/W1991069594","https://openalex.org/W2007717933","https://openalex.org/W2054629527","https://openalex.org/W2074099390","https://openalex.org/W2076068958","https://openalex.org/W2127108064","https://openalex.org/W2284126738","https://openalex.org/W2559085405","https://openalex.org/W2582112473","https://openalex.org/W2606294640","https://openalex.org/W2736279467","https://openalex.org/W2736334449","https://openalex.org/W2772635292","https://openalex.org/W2772973030","https://openalex.org/W2776687321","https://openalex.org/W2778523960","https://openalex.org/W2779252670","https://openalex.org/W2789406209","https://openalex.org/W2889508143","https://openalex.org/W2941398656","https://openalex.org/W2948246283","https://openalex.org/W2953787907","https://openalex.org/W2963076818","https://openalex.org/W2963369114","https://openalex.org/W2964134613","https://openalex.org/W2984598623","https://openalex.org/W2997769980","https://openalex.org/W2999180533","https://openalex.org/W3004228930","https://openalex.org/W3034999503","https://openalex.org/W3035050855","https://openalex.org/W3035225512","https://openalex.org/W3100437087","https://openalex.org/W3130243164","https://openalex.org/W4220982544","https://openalex.org/W4281770078","https://openalex.org/W6773070441"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W2964954556","https://openalex.org/W3029198973","https://openalex.org/W3019910406"],"abstract_inverted_index":{"Falls":[0],"pose":[1],"a":[2,14,24,28,86,132,167],"great":[3],"danger":[4],"to":[5,9,27,130,149,204],"social":[6],"development,":[7],"especially":[8],"the":[10,17,32,53,56,59,107,158,186],"elderly":[11],"population.":[12],"When":[13],"fall":[15,42,96,169],"occurs,":[16],"body's":[18],"center":[19],"of":[20,34,63,80,106],"gravity":[21],"moves":[22],"from":[23],"high":[25],"position":[26],"low":[29],"position,":[30],"and":[31,58,78,114,124,151,174,179,191,201],"magnitude":[33],"change":[35,60],"varies":[36],"among":[37],"body":[38,65,109],"parts.":[39],"Most":[40],"existing":[41,181],"recognition":[43,97],"methods":[44],"based":[45],"on":[46,166,175],"deep":[47,89],"learning":[48,90],"have":[49],"not":[50],"yet":[51],"considered":[52],"differences":[54],"between":[55],"movement":[57],"in":[61,100,172],"amplitude":[62],"each":[64],"part.":[66],"Besides,":[67],"some":[68],"problems":[69],"exist":[70],"such":[71],"as":[72],"complicated":[73],"design,":[74],"slow":[75],"detection":[76,200],"speed,":[77],"lack":[79],"timeliness.":[81],"To":[82],"alleviate":[83],"these":[84],"problems,":[85],"lightweight":[87,117,139],"subgraph-based":[88,118],"method":[91,163,188],"utilizing":[92],"skeleton":[93,104],"information":[94,105],"for":[95,198],"is":[98,110,120,146,164,189,196],"proposed":[99,162,187],"this":[101],"paper.":[102],"The":[103,161],"human":[108],"extracted":[111],"by":[112],"OpenPose,":[113],"an":[115],"end-to-end":[116],"network":[119],"designed.":[121],"Sub-graph":[122],"division":[123],"sub-graph":[125],"attention":[126],"modules":[127],"are":[128],"introduced":[129],"add":[131],"larger":[133],"perceptual":[134],"field":[135],"while":[136],"maintaining":[137],"its":[138],"characteristics.":[140],"A":[141],"multi-scale":[142,153],"temporal":[143,154],"convolution":[144],"module":[145],"also":[147],"designed":[148],"extract":[150],"fuse":[152],"features,":[155],"which":[156,193],"enriches":[157],"feature":[159],"representation.":[160],"evaluated":[165],"partial":[168],"dataset":[170],"collected":[171],"NTU":[173],"two":[176],"public":[177],"datasets,":[178],"outperforms":[180],"methods.":[182],"It":[183],"indicates":[184],"that":[185],"accurate":[190],"lightweight,":[192],"means":[194],"it":[195],"suitable":[197],"real-time":[199],"rapid":[202],"response":[203],"falls.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}