{"id":"https://openalex.org/W2136718936","doi":"https://doi.org/10.1145/2036146.2036149","title":"Context-aware fall detection using a Bayesian network","display_name":"Context-aware fall detection using a Bayesian network","publication_year":2011,"publication_date":"2011-09-17","ids":{"openalex":"https://openalex.org/W2136718936","doi":"https://doi.org/10.1145/2036146.2036149","mag":"2136718936"},"language":"en","primary_location":{"id":"doi:10.1145/2036146.2036149","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2036146.2036149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Workshop on Context-Awareness for Self-Managing Systems","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/A5100675021","display_name":"Mi Zhang","orcid":"https://orcid.org/0000-0003-1584-2799"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mi Zhang","raw_affiliation_strings":["University of Southern California, Los Angeles, CA","University of Southern California,,,Los Angeles,CA,"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"University of Southern California,,,Los Angeles,CA,","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015967023","display_name":"Alexander A. Sawchuk","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander A. Sawchuk","raw_affiliation_strings":["University of Southern California, Los Angeles, CA","University of Southern California,,,Los Angeles,CA,"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"University of Southern California,,,Los Angeles,CA,","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100675021"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.2614,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.61170432,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"10","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9983999729156494,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996399998664856,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7263634204864502},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6141002178192139},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.5996364951133728},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5548403263092041},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41610807180404663},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3374050259590149}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7263634204864502},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6141002178192139},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5996364951133728},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5548403263092041},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41610807180404663},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3374050259590149},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2036146.2036149","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2036146.2036149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Workshop on Context-Awareness for Self-Managing Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W95821903","https://openalex.org/W1511986666","https://openalex.org/W1965451324","https://openalex.org/W2009938720","https://openalex.org/W2053611092","https://openalex.org/W2072637932","https://openalex.org/W2073825700","https://openalex.org/W2093307231","https://openalex.org/W2101286829","https://openalex.org/W2102833071","https://openalex.org/W2103252230","https://openalex.org/W2110506616","https://openalex.org/W2122410182","https://openalex.org/W2397866408","https://openalex.org/W2537262575","https://openalex.org/W4229714860","https://openalex.org/W4254982405"],"related_works":["https://openalex.org/W2972991241","https://openalex.org/W2381390841","https://openalex.org/W2955463503","https://openalex.org/W3207148653","https://openalex.org/W4360591956","https://openalex.org/W2371925260","https://openalex.org/W2772917594","https://openalex.org/W4306742369","https://openalex.org/W4303457083","https://openalex.org/W2392577802"],"abstract_inverted_index":{"Human":[0],"activity":[1,113],"recognition":[2],"is":[3,142,216],"regarded":[4],"as":[5,105,136],"one":[6],"of":[7,28,120,165],"the":[8,146,163],"most":[9],"important":[10],"topics":[11],"in":[12,132,168,184],"ubiquitous":[13],"computing.":[14],"In":[15,68],"this":[16,69],"paper,":[17,70],"we":[18,71],"focus":[19,38],"on":[20,39,162],"recognizing":[21,133],"falls.":[22],"Falls":[23],"are":[24,122],"a":[25,53,66,88,96,137,166,169,181],"leading":[26],"cause":[27],"death":[29],"among":[30],"elderly":[31],"people.":[32],"Most":[33],"existing":[34],"fall":[35,42,75,81,151,167,186],"detection":[36,76,82,152,187],"techniques":[37],"studying":[40],"isolated":[41,80,150],"motion":[43],"under":[44],"restricted,":[45],"clearly":[46],"defined":[47],"conditions,":[48],"and":[49,115,154,159,189,194],"thus":[50],"suffer":[51],"from":[52],"relatively":[54],"high":[55],"false":[56,192,195],"positive":[57,196],"rate":[58],"induced":[59],"by":[60],"many":[61],"other":[62],"activities":[63,134],"that":[64,78,176,201],"resemble":[65],"fall.":[67,138],"present":[72],"an":[73],"integrated":[74],"framework":[77,127],"incorporates":[79],"algorithms":[83],"with":[84],"context":[85,92,177],"information":[86,93,121,178,215],"using":[87],"Bayesian":[89,140,204],"network.":[90],"The":[91],"can":[94,179,206],"include":[95],"person's":[97],"age,":[98],"personal":[99],"health":[100],"history,":[101],"physiological":[102],"measurements":[103],"(such":[104],"respiration,":[106],"blood":[107],"pressure,":[108],"heart":[109],"rate,":[110],"etc.),":[111],"physical":[112],"level":[114],"location.":[116],"These":[117],"additional":[118],"sources":[119],"complement":[123],"inputs":[124],"to":[125,128,144],"our":[126,202],"improve":[129],"decision":[130],"accuracy":[131,188],"such":[135],"A":[139],"network":[141],"constructed":[143],"structure":[145],"probabilistic":[147,203],"dependencies":[148],"between":[149],"result":[153],"various":[155],"contextual":[156,214],"sensor":[157],"readings,":[158],"perform":[160],"inference":[161,209],"likelihood":[164],"given":[170],"context.":[171],"Preliminary":[172],"experimental":[173],"results":[174,210],"demonstrate":[175,200],"play":[180],"significant":[182],"role":[183],"improving":[185],"reducing":[190],"both":[191],"negative":[193],"rates.":[197],"We":[198],"also":[199],"model":[205],"produce":[207],"informative":[208],"even":[211],"when":[212],"partial":[213],"observed.":[217]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}