{"id":"https://openalex.org/W4399478992","doi":"https://doi.org/10.1117/12.3013776","title":"Human pose estimation and gait analysis with convolutional neural networks for Alzheimer\u2019s disease detection","display_name":"Human pose estimation and gait analysis with convolutional neural networks for Alzheimer\u2019s disease detection","publication_year":2024,"publication_date":"2024-06-10","ids":{"openalex":"https://openalex.org/W4399478992","doi":"https://doi.org/10.1117/12.3013776"},"language":"en","primary_location":{"id":"doi:10.1117/12.3013776","doi":"https://doi.org/10.1117/12.3013776","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3013776","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data VI: Learning, Analytics, and Applications","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5103003570","display_name":"Mahmoud Seifallahi","orcid":"https://orcid.org/0000-0002-6056-2115","observed_orcids":["https://orcid.org/0000-0002-6056-2115"]},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mahmoud Seifallahi","raw_affiliation_strings":["Florida Atlantic Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic Univ. (United States)","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107517041","display_name":"Brennen Farrell","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brennen Farrell","raw_affiliation_strings":["Florida Atlantic Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic Univ. (United States)","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084326649","display_name":"James E. Galvin","orcid":"https://orcid.org/0000-0002-4561-0318","observed_orcids":["https://orcid.org/0000-0002-4561-0318"]},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James E. Galvin","raw_affiliation_strings":["Univ. of Miami (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Miami (United States)","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032994571","display_name":"Behnaz Ghoraani","orcid":"https://orcid.org/0000-0003-0075-7663","observed_orcids":["https://orcid.org/0000-0003-0075-7663"]},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Behnaz Ghoraani","raw_affiliation_strings":["Florida Atlantic Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic Univ. (United States)","institution_ids":["https://openalex.org/I63772739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8838,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81867129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"21","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.46970000863075256,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.46970000863075256,"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":0.36550000309944153,"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/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.05900000035762787,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"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/alzheimer-s-disease-detection","display_name":"Alzheimer's disease detection","score":0.9629999995231628},{"id":"https://openalex.org/keywords/human-pose-estimation","display_name":"human pose estimation","score":0.9290000200271606},{"id":"https://openalex.org/keywords/gait-analysis","display_name":"gait analysis","score":0.9950000047683716},{"id":"https://openalex.org/keywords/openpose","display_name":"OpenPose","score":0.7929999828338623},{"id":"https://openalex.org/keywords/gait-parameters","display_name":"gait parameters","score":0.9210000038146973},{"id":"https://openalex.org/keywords/early-diagnosis","display_name":"early diagnosis","score":0.5059999823570251},{"id":"https://openalex.org/keywords/home-monitoring","display_name":"home monitoring","score":0.47699999809265137},{"id":"https://openalex.org/keywords/older-adults","display_name":"older adults","score":0.5669999718666077}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7629917860031128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7212002873420715},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.6274756193161011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6126781702041626},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5692267417907715},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.4396364688873291},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40794503688812256},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39948099851608276},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.2750580310821533},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15091195702552795}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.3013776","doi":"https://doi.org/10.1117/12.3013776","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.3013776","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data VI: Learning, Analytics, and Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9922999739646912,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2084397926","https://openalex.org/W2157972276","https://openalex.org/W2165631177","https://openalex.org/W2322008730","https://openalex.org/W2563305050","https://openalex.org/W2791546877","https://openalex.org/W2888202680","https://openalex.org/W2899971745","https://openalex.org/W3010559627","https://openalex.org/W3093156138","https://openalex.org/W3154625726","https://openalex.org/W3157356727","https://openalex.org/W3205816089","https://openalex.org/W4205810641","https://openalex.org/W4226512252","https://openalex.org/W4238582840","https://openalex.org/W4282932577","https://openalex.org/W4312665611","https://openalex.org/W4360770700","https://openalex.org/W4365457400","https://openalex.org/W4386159886","https://openalex.org/W4388629661","https://openalex.org/W4390481113","https://openalex.org/W6859813833"],"related_works":["https://openalex.org/W4405489634","https://openalex.org/W4410999974","https://openalex.org/W3003160069","https://openalex.org/W4391306835","https://openalex.org/W7168663480","https://openalex.org/W4406886138","https://openalex.org/W4409958146","https://openalex.org/W2986884899","https://openalex.org/W4226225523","https://openalex.org/W4206071207"],"abstract_inverted_index":{"In":[0,229],"computer":[1],"vision,":[2],"human":[3],"pose":[4,90],"estimation":[5],"(HPE)":[6],"through":[7],"convolutional":[8],"neural":[9],"networks":[10],"(CNNs)":[11],"has":[12],"emerged":[13],"as":[14],"a":[15,24,37,76,103,122,125,141,194,215],"promising":[16],"avenue":[17],"with":[18],"broad":[19],"applicability.":[20],"This":[21],"study":[22],"bridges":[23],"novel":[25],"application":[26],"of":[27,33,107,127,182,205,219,224,237],"HPE,":[28],"targeting":[29],"the":[30,136,158,183,188,201,203,235,250],"early":[31],"detection":[32,255],"Alzheimer\u2019s":[34],"disease":[35],"(AD),":[36],"condition":[38],"expected":[39],"to":[40,82,135,171,199],"affect":[41],"roughly":[42],"13.4":[43],"million":[44],"Americans":[45],"by":[46],"2026.":[47],"Traditionally,":[48],"AD":[49,116,191,227,254],"diagnostic":[50],"methodologies":[51],"like":[52],"brain":[53],"imaging,":[54],"Electroencephalography,":[55],"and":[56,63,78,88,94,114,169,190,221,245],"blood/neuropsychological":[57],"tests":[58],"are":[59],"not":[60],"only":[61],"expensive":[62],"protracted":[64],"but":[65],"also":[66],"require":[67],"specialized":[68],"medical":[69],"expertise.":[70],"Addressing":[71],"these":[72,167,206],"constraints,":[73],"we":[74,150],"introduce":[75],"cost-efficient":[77],"universally":[79],"accessible":[80],"system":[81,213],"detect":[83],"AD,":[84],"harnessing":[85],"conventional":[86],"cameras":[87],"employing":[89],"estimation,":[91],"signal":[92,243],"processing,":[93,244],"machine":[95,246],"learning.":[96],"Data":[97],"was":[98,121,177,209],"sourced":[99],"from":[100,157,166],"videos":[101],"capturing":[102],"10-meter":[104],"curve":[105],"walk":[106],"73":[108],"cognitively":[109],"healthy":[110],"older":[111],"adults":[112],"(HC)":[113],"34":[115],"patients.":[117],"The":[118,212],"recording":[119],"apparatus":[120],"camera":[123],"offering":[124],"resolution":[126],"1920x1080":[128],"pixels":[129],"at":[130],"30":[131],"frames/second,":[132],"stationed":[133],"laterally":[134],"walking":[137],"path.":[138],"Using":[139],"OpenPose,":[140],"state-of-the-art,":[142],"bottom-up":[143],"multi-person":[144],"HPE":[145,241],"method":[146],"based":[147],"on":[148],"CNNs,":[149],"derived":[151],"25":[152],"distinctive":[153],"body":[154],"joint":[155],"coordinates":[156],"footage.":[159],"Subsequently,":[160],"48":[161,184],"gait":[162,185,207],"parameters":[163,186],"were":[164],"extracted":[165],"joints":[168],"subjected":[170],"statistical":[172],"scrutiny.":[173],"A":[174],"noticeable":[175],"difference":[176],"observed":[178],"in":[179,256],"39":[180],"out":[181],"between":[187],"HC":[189],"groups.":[192],"Leveraging":[193],"Support":[195],"Vector":[196],"Machine":[197],"(SVM)":[198],"classify":[200],"data,":[202],"distinctiveness":[204],"markers":[208],"further":[210],"affirmed.":[211],"accomplished":[214],"commendable":[216],"accuracy":[217],"rate":[218],"90.01%":[220],"an":[222],"F-score":[223],"86.20%":[225],"for":[226,252],"identification.":[228],"essence,":[230],"our":[231],"findings":[232],"advocate":[233],"that":[234],"amalgamation":[236],"everyday":[238],"cameras,":[239],"sophisticated":[240],"techniques,":[242],"learning":[247],"can":[248],"pave":[249],"way":[251],"practical":[253],"non-specialized":[257],"settings,":[258],"including":[259],"home":[260],"environments.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2024-06-11T00:00:00"}