{"id":"https://openalex.org/W2292776526","doi":"https://doi.org/10.1109/robio.2015.7418773","title":"An improved classification method for fall detection based on Bayesian framework","display_name":"An improved classification method for fall detection based on Bayesian framework","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2292776526","doi":"https://doi.org/10.1109/robio.2015.7418773","mag":"2292776526"},"language":"en","primary_location":{"id":"doi:10.1109/robio.2015.7418773","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robio.2015.7418773","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)","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/A5089770704","display_name":"Jie Li","orcid":"https://orcid.org/0000-0002-4063-9381"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Li","raw_affiliation_strings":["School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100719011","display_name":"Min Li","orcid":"https://orcid.org/0000-0001-8140-934X"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Li","raw_affiliation_strings":["School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044246307","display_name":"Zhongya Wang","orcid":"https://orcid.org/0000-0002-5139-261X"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongya Wang","raw_affiliation_strings":["School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101461575","display_name":"Qingying Zhao","orcid":"https://orcid.org/0000-0001-8846-6152"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingying Zhao","raw_affiliation_strings":["School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3744,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70837435,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"237","last_page":"242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9702000021934509,"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/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.9549999833106995,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6819167137145996},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6724018454551697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.638828456401825},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6236692667007446},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.504904568195343},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5020465850830078},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.5009281635284424},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4678923189640045},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4601903557777405},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.42006564140319824},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.41116976737976074},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38835227489471436}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819167137145996},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6724018454551697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.638828456401825},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6236692667007446},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.504904568195343},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5020465850830078},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.5009281635284424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4678923189640045},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4601903557777405},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.42006564140319824},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.41116976737976074},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38835227489471436}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/robio.2015.7418773","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robio.2015.7418773","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1512614806","https://openalex.org/W1566752807","https://openalex.org/W1671410324","https://openalex.org/W1984063679","https://openalex.org/W2054480530","https://openalex.org/W2067454711","https://openalex.org/W2072130063","https://openalex.org/W2084812512","https://openalex.org/W2086137009","https://openalex.org/W2105550216","https://openalex.org/W2109362352","https://openalex.org/W2156571267","https://openalex.org/W2170672895","https://openalex.org/W2534614379","https://openalex.org/W2541166653","https://openalex.org/W4285719527","https://openalex.org/W6668436134","https://openalex.org/W6682904970","https://openalex.org/W6684960675"],"related_works":["https://openalex.org/W2998914036","https://openalex.org/W2388326001","https://openalex.org/W1977671905","https://openalex.org/W2390565185","https://openalex.org/W1983393909","https://openalex.org/W2040150569","https://openalex.org/W2468095590","https://openalex.org/W2132174924","https://openalex.org/W1911540634","https://openalex.org/W2013909972"],"abstract_inverted_index":{"Aging":[0],"population":[1],"is":[2,78],"a":[3,21,144,149],"big":[4],"challenge":[5],"in":[6],"modern":[7],"healthcare.":[8],"Injuries":[9],"caused":[10],"by":[11],"fall":[12,23,88],"incidents":[13],"are":[14,67,114],"great":[15],"threats":[16],"to":[17,39,96],"the":[18,41,49,52,98,104,108,117,121,126,138],"elderly.":[19],"As":[20],"consequence,":[22],"detection":[24,146],"and":[25,33,47,87,120,125,148],"movement":[26],"classification":[27,105,129,140],"have":[28],"very":[29],"high":[30],"research":[31],"value":[32],"application":[34],"significance.":[35],"This":[36],"paper":[37],"aims":[38],"study":[40],"optimum":[42],"feature":[43,85],"subset":[44],"of":[45,51,59,62,65,100,111,128],"falls":[46],"verify":[48],"effectiveness":[50],"proposed":[53,79],"features":[54,61,92,101,119],"through":[55],"experiments.":[56],"A":[57],"set":[58],"twelve":[60],"eleven":[63],"kinds":[64],"activities":[66],"extracted":[68],"from":[69],"four":[70],"sensor":[71],"data":[72],"firstly.":[73],"Then,":[74],"an":[75],"improved":[76,139],"classifier":[77,122],"based":[80],"on":[81],"Bayesian":[82],"framework":[83],"for":[84,103],"selection":[86],"detection.":[89],"The":[90],"optimal":[91,118],"will":[93,130],"be":[94,131],"selected":[95],"reduce":[97],"number":[99],"required":[102],"problem.":[106],"Finally,":[107],"activity":[109],"types":[110],"unknown":[112],"samples":[113],"predicted":[115],"using":[116],"gained":[123],"above,":[124],"accuracy":[127],"analyzed.":[132],"It":[133],"has":[134],"been":[135],"verified":[136],"that":[137],"method":[141],"can":[142],"get":[143],"higher":[145],"rate":[147],"lower":[150],"false":[151],"alarm":[152],"rate.":[153]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}