{"id":"https://openalex.org/W2546856665","doi":"https://doi.org/10.1109/acpr.2013.124","title":"Detecting Fall Incidents of the Elderly Based on Human-Ground Contact Areas","display_name":"Detecting Fall Incidents of the Elderly Based on Human-Ground Contact Areas","publication_year":2013,"publication_date":"2013-11-01","ids":{"openalex":"https://openalex.org/W2546856665","doi":"https://doi.org/10.1109/acpr.2013.124","mag":"2546856665"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2013.124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2013.124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 2nd IAPR Asian Conference on Pattern Recognition","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/A5110972356","display_name":"Dao Huu Hung","orcid":null},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Dao Huu Hung","raw_affiliation_strings":["Department of Information and Computer Science, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Computer Science, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005819073","display_name":"Hideo Sait\u00f4","orcid":"https://orcid.org/0000-0002-2421-9862"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideo Saito","raw_affiliation_strings":["Department of Information and Computer Science, Keio University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Computer Science, Keio University, Yokohama, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015206948","display_name":"Gee-Sern Hsu","orcid":"https://orcid.org/0000-0003-2631-0448"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Gee-Sern Hsu","raw_affiliation_strings":["Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan","institution_ids":["https://openalex.org/I154864474"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1051,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.82670607,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"4","issue":null,"first_page":"516","last_page":"521"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9991999864578247,"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":0.998199999332428,"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/lying","display_name":"Lying","score":0.9463478326797485},{"id":"https://openalex.org/keywords/homography","display_name":"Homography","score":0.6504284143447876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.646119236946106},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6139605641365051},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6004201173782349},{"id":"https://openalex.org/keywords/ground-plane","display_name":"Ground plane","score":0.5454356074333191},{"id":"https://openalex.org/keywords/parallax","display_name":"Parallax","score":0.519548237323761},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.43815428018569946},{"id":"https://openalex.org/keywords/sitting","display_name":"Sitting","score":0.4191874563694},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.41494399309158325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22868022322654724},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1174359917640686},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08364471793174744}],"concepts":[{"id":"https://openalex.org/C119421448","wikidata":"https://www.wikidata.org/wiki/Q2268776","display_name":"Lying","level":2,"score":0.9463478326797485},{"id":"https://openalex.org/C28751775","wikidata":"https://www.wikidata.org/wiki/Q2112539","display_name":"Homography","level":4,"score":0.6504284143447876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.646119236946106},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6139605641365051},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6004201173782349},{"id":"https://openalex.org/C88764893","wikidata":"https://www.wikidata.org/wiki/Q1547722","display_name":"Ground plane","level":3,"score":0.5454356074333191},{"id":"https://openalex.org/C15759828","wikidata":"https://www.wikidata.org/wiki/Q165074","display_name":"Parallax","level":2,"score":0.519548237323761},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.43815428018569946},{"id":"https://openalex.org/C2776370487","wikidata":"https://www.wikidata.org/wiki/Q1144593","display_name":"Sitting","level":2,"score":0.4191874563694},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.41494399309158325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22868022322654724},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1174359917640686},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08364471793174744},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C75280867","wikidata":"https://www.wikidata.org/wiki/Q877775","display_name":"Projective space","level":3,"score":0.0},{"id":"https://openalex.org/C21822782","wikidata":"https://www.wikidata.org/wiki/Q131214","display_name":"Antenna (radio)","level":2,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C177846678","wikidata":"https://www.wikidata.org/wiki/Q1501864","display_name":"Projective test","level":2,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/acpr.2013.124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2013.124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 2nd IAPR Asian Conference on Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.664.3377","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.664.3377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://hvrl.ics.keio.ac.jp/paper/pdf/international_Conference/2013/Dao_ACPR2013.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1539154803","https://openalex.org/W1986387010","https://openalex.org/W2028191869","https://openalex.org/W2030972883","https://openalex.org/W2032791935","https://openalex.org/W2033819227","https://openalex.org/W2064478533","https://openalex.org/W2067454711","https://openalex.org/W2072130063","https://openalex.org/W2089528460","https://openalex.org/W2091566335","https://openalex.org/W2115343816","https://openalex.org/W2116353056","https://openalex.org/W2118218997","https://openalex.org/W2120730550","https://openalex.org/W2131889717","https://openalex.org/W2150216628","https://openalex.org/W2151453382","https://openalex.org/W2170672895","https://openalex.org/W2209124607","https://openalex.org/W4251262448","https://openalex.org/W6632268950","https://openalex.org/W6668436134","https://openalex.org/W6678367174","https://openalex.org/W6682484900"],"related_works":["https://openalex.org/W2955910435","https://openalex.org/W2888123326","https://openalex.org/W2501458630","https://openalex.org/W2982312151","https://openalex.org/W4246089694","https://openalex.org/W2747462053","https://openalex.org/W2753162950","https://openalex.org/W4210719352","https://openalex.org/W2347526077","https://openalex.org/W1489221476"],"abstract_inverted_index":{"People":[0],"always":[1],"make":[2],"a":[3,48,66,136,204],"little":[4],"contact":[5,122],"with":[6,123,152,207],"the":[7,20,99,111,124,145,184,212],"ground":[8,21,100],"during":[9],"usual":[10,79],"activities":[11],"mainly":[12],"by":[13,47,95],"feet":[14],"but":[15],"often":[16],"lie":[17],"completely":[18],"on":[19,197,211],"after":[22],"accidental":[23],"falls.":[24],"Thus,":[25],"we":[26,84],"propose":[27,158],"using":[28,96,159],"Human-Ground":[29],"Contact":[30],"Areas":[31],"(HGCA)":[32],"to":[33,58,75,93,127,140,154,170,189,203],"classify":[34],"human":[35,52,155,160,173],"states":[36,53,143],"into":[37],"standing,":[38],"sitting":[39,57],"and":[40,60,108],"lying":[41,59,63,142],"states.":[42,156,177],"A":[43],"fall":[44,200],"is":[45,73],"defined":[46],"fast":[49],"change":[50],"of":[51,87,98,138,150,179],"from":[54,78,90,144,147,183],"standing":[55],"or":[56],"continuity":[61],"in":[62,110,117,121,163,175],"state":[64,161],"for":[65],"sufficient":[67],"duration.":[68],"Temporal":[69],"analyzing":[70],"human-state":[71],"transitions":[72],"used":[74],"discriminate":[76],"falls":[77],"events.":[80],"To":[81],"measure":[82],"HGCA,":[83],"project":[85],"foreground":[86,107,109],"monitored":[88],"person":[89],"one":[91],"view":[92,113],"another":[94],"homography":[97],"between":[101,105],"them.":[102],"Overlap":[103],"regions":[104],"projected":[106],"latter":[112],"that":[114],"only":[115],"exist":[116],"which":[118,164],"people":[119],"are":[120,130,167,181],"ground,":[125],"due":[126],"plane":[128],"parallax,":[129],"measured":[131],"as":[132,186],"HGCA.":[133],"We":[134,157,193],"generalize":[135],"threshold":[137],"HGCA":[139,151],"separate":[141],"others":[146],"view-invariant":[148],"distributions":[149],"respect":[153],"simulation":[162,185],"camera":[165,199],"viewpoints":[166],"freely":[168],"changed":[169],"capture":[171],"3D":[172],"models":[174],"various":[176],"Hundreds":[178],"images":[180],"generated":[182],"training":[187],"data":[188],"build":[190],"these":[191],"distributions.":[192],"test":[194],"our":[195],"method":[196],"\"multiple":[198],"dataset\"":[201],"leading":[202],"competitive":[205],"performance":[206],"other":[208],"methods":[209],"tested":[210],"same":[213],"dataset.":[214]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}