{"id":"https://openalex.org/W2331491382","doi":"https://doi.org/10.4108/eai.14-10-2015.2261619","title":"A Cascade-Classifier Approach for Fall Detection","display_name":"A Cascade-Classifier Approach for Fall Detection","publication_year":2015,"publication_date":"2015-12-22","ids":{"openalex":"https://openalex.org/W2331491382","doi":"https://doi.org/10.4108/eai.14-10-2015.2261619","mag":"2331491382"},"language":"en","primary_location":{"id":"doi:10.4108/eai.14-10-2015.2261619","is_oa":true,"landing_page_url":"https://doi.org/10.4108/eai.14-10-2015.2261619","pdf_url":"http://eudl.eu/pdf/10.4108/eai.14-10-2015.2261619","source":{"id":"https://openalex.org/S2736858385","display_name":"EAI Endorsed Transactions on Pervasive Health and Technology","issn_l":"2411-7145","issn":["2411-7145"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321144","host_organization_name":"Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","host_organization_lineage":["https://openalex.org/P4310321144"],"host_organization_lineage_names":["Institute for Computer Sciences, Social Informatics and Telecommunications Engineering"],"type":"journal"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EAI Endorsed Transactions on Pervasive Health and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"http://eudl.eu/pdf/10.4108/eai.14-10-2015.2261619","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111593905","display_name":"I Putu Edy Suardiyana Putra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"I Putu Edy Suardiyana Putra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066204863","display_name":"James Brusey","orcid":"https://orcid.org/0000-0002-2710-6927"},"institutions":[{"id":"https://openalex.org/I4210156582","display_name":"Cogent (United Kingdom)","ror":"https://ror.org/05g0b2873","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210156582"]},{"id":"https://openalex.org/I73417466","display_name":"Coventry University","ror":"https://ror.org/01tgmhj36","country_code":"GB","type":"education","lineage":["https://openalex.org/I73417466"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"James Brusey","raw_affiliation_strings":["Cogent Labs Coventry University Coventry, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cogent Labs Coventry University Coventry, United Kingdom","institution_ids":["https://openalex.org/I73417466","https://openalex.org/I4210156582"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024282033","display_name":"Elena Gaura","orcid":"https://orcid.org/0000-0003-2943-2037"},"institutions":[{"id":"https://openalex.org/I4210156582","display_name":"Cogent (United Kingdom)","ror":"https://ror.org/05g0b2873","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210156582"]},{"id":"https://openalex.org/I73417466","display_name":"Coventry University","ror":"https://ror.org/01tgmhj36","country_code":"GB","type":"education","lineage":["https://openalex.org/I73417466"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Elena Gaura","raw_affiliation_strings":["Cogent Labs Coventry University Coventry, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cogent Labs Coventry University Coventry, United Kingdom","institution_ids":["https://openalex.org/I73417466","https://openalex.org/I4210156582"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5616,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76826147,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2","issue":"8","first_page":"e2","last_page":"e2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9864000082015991,"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"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9825999736785889,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.6195562481880188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5385369658470154},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5067372918128967},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46889036893844604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4071713387966156},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16850361227989197}],"concepts":[{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.6195562481880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5385369658470154},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5067372918128967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46889036893844604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4071713387966156},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16850361227989197},{"id":"https://openalex.org/C42360764","wikidata":"https://www.wikidata.org/wiki/Q83588","display_name":"Chemical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.4108/eai.14-10-2015.2261619","is_oa":true,"landing_page_url":"https://doi.org/10.4108/eai.14-10-2015.2261619","pdf_url":"http://eudl.eu/pdf/10.4108/eai.14-10-2015.2261619","source":{"id":"https://openalex.org/S2736858385","display_name":"EAI Endorsed Transactions on Pervasive Health and Technology","issn_l":"2411-7145","issn":["2411-7145"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321144","host_organization_name":"Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","host_organization_lineage":["https://openalex.org/P4310321144"],"host_organization_lineage_names":["Institute for Computer Sciences, Social Informatics and Telecommunications Engineering"],"type":"journal"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EAI Endorsed Transactions on Pervasive Health and Technology","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire/26a6f345-4748-4739-9750-432d0496587b","is_oa":true,"landing_page_url":"https://pureportal.coventry.ac.uk/en/publications/26a6f345-4748-4739-9750-432d0496587b","pdf_url":"https://pure.coventry.ac.uk/ws/files/40237230/Binder4.pdf","source":{"id":"https://openalex.org/S4306402411","display_name":"Pure (Coventry University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I73417466","host_organization_name":"Coventry University","host_organization_lineage":["https://openalex.org/I73417466"],"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":"Putu Edy Suardiyana Putra, I, Brusey, J & Gaura, E 2015, A cascade-classifier approach for fall detection. in A Alomainy, Y Hao, W Whittow, K S Nikita & C G Parini (eds), MOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies. MOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies, ICST, Brussels, pp. 94-99, 5th EAI International Conference on Wireless Mobile Communication and Healthcare, London, United Kingdom, 14/10/15. https://doi.org/10.4108/eai.14-10-2015.2261619","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:doaj.org/article:08454113801e4695b63f6d5fefdcc1e1","is_oa":true,"landing_page_url":"https://doaj.org/article/08454113801e4695b63f6d5fefdcc1e1","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":"EAI Endorsed Transactions on Pervasive Health and Technology, Vol 2, Iss 8 (2016)","raw_type":"article"},{"id":"pmh:oai:pure.atira.dk:publications/26a6f345-4748-4739-9750-432d0496587b","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85034990265&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4306402411","display_name":"Pure (Coventry University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I73417466","host_organization_name":"Coventry University","host_organization_lineage":["https://openalex.org/I73417466"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Putu Edy Suardiyana Putra , I , Brusey , J &amp; Gaura , E 2015 , A cascade-classifier approach for fall detection . in A Alomainy , Y Hao , W Whittow , K S Nikita &amp; C G Parini (eds) , MOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies . MOBIHEALTH 2015 - 5th EAI International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies , ICST , Brussels , pp. 94-99 , 5th EAI International Conference on Wireless Mobile Communication and Healthcare , London , United Kingdom , 14/10/15 . https://doi.org/10.4108/eai.14-10-2015.2261619","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"doi:10.4108/eai.14-10-2015.2261619","is_oa":true,"landing_page_url":"https://doi.org/10.4108/eai.14-10-2015.2261619","pdf_url":"http://eudl.eu/pdf/10.4108/eai.14-10-2015.2261619","source":{"id":"https://openalex.org/S2736858385","display_name":"EAI Endorsed Transactions on Pervasive Health and Technology","issn_l":"2411-7145","issn":["2411-7145"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321144","host_organization_name":"Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","host_organization_lineage":["https://openalex.org/P4310321144"],"host_organization_lineage_names":["Institute for Computer Sciences, Social Informatics and Telecommunications Engineering"],"type":"journal"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EAI Endorsed Transactions on Pervasive Health and Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2331491382.pdf","grobid_xml":"https://content.openalex.org/works/W2331491382.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W755043767","https://openalex.org/W1971422403","https://openalex.org/W1972172400","https://openalex.org/W1976313253","https://openalex.org/W1994504763","https://openalex.org/W2009559600","https://openalex.org/W2017803470","https://openalex.org/W2022974596","https://openalex.org/W2051097274","https://openalex.org/W2085478833","https://openalex.org/W2088275437","https://openalex.org/W2133922998","https://openalex.org/W2133990480","https://openalex.org/W2148048965","https://openalex.org/W2152449912","https://openalex.org/W2155326828","https://openalex.org/W2156593015","https://openalex.org/W2159825898","https://openalex.org/W2163660327","https://openalex.org/W3141916467","https://openalex.org/W6642936320"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2153719181","https://openalex.org/W1971748923","https://openalex.org/W1566155057","https://openalex.org/W2060986072","https://openalex.org/W2052574922","https://openalex.org/W64588465","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"The":[0,100],"current":[1],"machine":[2],"learning":[3,114],"algorithms":[4],"in":[5],"fall":[6,64],"detection,":[7],"especially":[8],"those":[9],"that":[10,38,57,103],"use":[11],"a":[12,16,54,77],"sliding":[13],"window,":[14],"have":[15],"high":[17],"computational":[18,60,110],"cost":[19,61,111],"because":[20],"they":[21],"need":[22],"to":[23,86],"compute":[24],"the":[25,39,59,63,104,109,115,123,127],"features":[26],"from":[27,73],"almost":[28],"all":[29],"samples.":[30],"This":[31,51],"computation":[32],"causes":[33],"energy":[34],"drain":[35],"and":[36,81,97,117,137],"means":[37],"associated":[40],"wearable":[41],"devices":[42],"re-":[43],"quire":[44],"frequent":[45],"recharging,":[46],"making":[47],"them":[48],"less":[49],"usable.":[50],"study":[52],"proposes":[53],"cascade":[55,105],"approach":[56,106],"reduces":[58,108],"of":[62,79,90,132,135,139],"detection":[65],"classifier.":[66],"To":[67],"examine":[68],"this":[69],"approach,":[70],"accelerometer":[71],"data":[72],"48":[74],"subjects":[75],"performing":[76],"combination":[78],"falls":[80],"ordinary":[82],"behaviour":[83],"is":[84],"used":[85],"train":[87],"3":[88],"types":[89],"classifier":[91,116],"(J48":[92],"Decision":[93],"Tree,":[94],"Logistic":[95],"Regression,":[96],"Multilayer":[98,124],"Perceptron).":[99],"results":[101],"show":[102],"significantly":[107],"both":[112],"for":[113],"executing":[118],"it":[119],"once":[120],"learnt.":[121],"Furthermore,":[122],"Perceptron":[125],"achieves":[126],"highest":[128],"performance":[129],"with":[130],"precision":[131],"93.5%,":[133],"recall":[134],"94.2%,":[136],"f-measure":[138],"93.5%.":[140]},"counts_by_year":[{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
