{"id":"https://openalex.org/W4294672824","doi":"https://doi.org/10.1109/rcar54675.2022.9872285","title":"Combining Clustering Undersample and Ensemble Learning for Wearable Fall Detection","display_name":"Combining Clustering Undersample and Ensemble Learning for Wearable Fall Detection","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4294672824","doi":"https://doi.org/10.1109/rcar54675.2022.9872285"},"language":"en","primary_location":{"id":"doi:10.1109/rcar54675.2022.9872285","doi":"https://doi.org/10.1109/rcar54675.2022.9872285","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar54675.2022.9872285","pdf_url":null,"source":{"id":"https://openalex.org/S4363608312","display_name":"IEEE International Conference on Real-time Computing and Robotics (RCAR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5100691587","display_name":"Meng Zhang","orcid":"https://orcid.org/0009-0002-2551-8271","observed_orcids":["https://orcid.org/0009-0002-2551-8271"]},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang Meng","raw_affiliation_strings":["Beijing University of Technology,Faculty of Information Technology,Beijing,China","Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Faculty of Information Technology,Beijing,China","institution_ids":["https://openalex.org/I37796252"]},{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010150668","display_name":"Daoxiong Gong","orcid":"https://orcid.org/0000-0003-2492-9159","observed_orcids":["https://orcid.org/0000-0003-2492-9159"]},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daoxiong Gong","raw_affiliation_strings":["Beijing University of Technology,Faculty of Information Technology,Beijing,China","Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Technology,Faculty of Information Technology,Beijing,China","institution_ids":["https://openalex.org/I37796252"]},{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":null,"apc_paid":null,"fwci":0.3229,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61477534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"39","issue":null,"first_page":"545","last_page":"550"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.7846999764442444,"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":0.7846999764442444,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.18529999256134033,"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/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.016699999570846558,"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/fall-detection","display_name":"fall detection","score":0.9509999752044678},{"id":"https://openalex.org/keywords/wearable-sensors","display_name":"wearable sensors","score":0.9789999723434448},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.8809999823570251},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.9990000128746033},{"id":"https://openalex.org/keywords/elderly-safety","display_name":"elderly safety","score":0.6710000038146973},{"id":"https://openalex.org/keywords/sisfall-dataset","display_name":"SisFall dataset","score":0.7549999952316284},{"id":"https://openalex.org/keywords/binary-classification","display_name":"binary classification","score":0.6850000023841858}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9450792074203491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7618945837020874},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7346349954605103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7012603282928467},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6917585134506226},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4983651638031006},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4684933125972748},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42733463644981384},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42111167311668396},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3616529107093811}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rcar54675.2022.9872285","doi":"https://doi.org/10.1109/rcar54675.2022.9872285","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar54675.2022.9872285","pdf_url":null,"source":{"id":"https://openalex.org/S4363608312","display_name":"IEEE International Conference on Real-time Computing and Robotics (RCAR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4043000042438507,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334977","display_name":"Beijing Municipal Natural Science Foundation","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2017794574","https://openalex.org/W2020957915","https://openalex.org/W2104167780","https://openalex.org/W2107308147","https://openalex.org/W2192249782","https://openalex.org/W2295598076","https://openalex.org/W2414900880","https://openalex.org/W2543632018","https://openalex.org/W2770106921","https://openalex.org/W2773078063","https://openalex.org/W2797186212","https://openalex.org/W2886857295","https://openalex.org/W2891151898","https://openalex.org/W2896965072","https://openalex.org/W2914888512","https://openalex.org/W2936781770","https://openalex.org/W2938926494","https://openalex.org/W2966205757","https://openalex.org/W3003591518","https://openalex.org/W3133493237","https://openalex.org/W3164845984"],"related_works":["https://openalex.org/W4310736190","https://openalex.org/W3201969843","https://openalex.org/W3127240170","https://openalex.org/W4288097446","https://openalex.org/W4396668750","https://openalex.org/W2161464748","https://openalex.org/W7133330062","https://openalex.org/W7221786568","https://openalex.org/W3167787205","https://openalex.org/W3116980050"],"abstract_inverted_index":{"Accidental":[0],"falls":[1,15],"often":[2],"cause":[3,68],"serious":[4],"harm":[5],"to":[6,17,23,54,62,74],"the":[7,12,39,69,72,82,92,110,136,142,154,159,174,180,185,197,201],"human":[8],"body,":[9],"especially":[10],"for":[11,29,127],"elderly.":[13],"But":[14],"tend":[16],"be":[18,75],"infrequent,":[19],"making":[20],"it":[21],"difficult":[22],"collect":[24],"large":[25,36],"amounts":[26],"of":[27,41,71,85,94,112,138,161],"data":[28,43,106],"research.":[30],"In":[31,114],"reality,":[32],"there":[33],"is":[34,171],"a":[35,86,204],"gap":[37],"between":[38],"amount":[40],"sensor":[42],"collected":[44],"by":[45],"falling":[46],"activities":[47],"and":[48,80,104,121,157,163,191,207],"daily":[49],"activities,":[50],"which":[51],"will":[52,67],"lead":[53],"class":[55,65,96],"imbalance.":[56],"When":[57,89],"using":[58],"machine":[59,101,182],"learning":[60,102,119,132,183,187],"algorithms":[61],"detect":[63],"falls,":[64],"imbalance":[66],"performance":[70],"classifier":[73,147],"biased":[76],"towards":[77],"most":[78],"classes":[79],"reduce":[81,135],"detection":[83],"accuracy":[84,111,190],"few":[87],"classes.":[88],"faced":[90],"with":[91,179,196],"problem":[93],"binary":[95],"imbalance,":[97],"selecting":[98],"an":[99,117],"effective":[100],"algorithm":[103,120,133],"resampling":[105],"can":[107,134,152],"effectively":[108],"improve":[109],"classification.":[113],"this":[115,169],"paper,":[116],"ensemble":[118,131,186],"clustering":[122,198],"undersampling":[123,150,199],"method":[124,151,167,202],"are":[125],"used":[126],"fall":[128],"detection.":[129],"The":[130,166],"impact":[137],"imbalanced":[139],"datasets":[140],"on":[141,173],"training":[143,193],"model":[144],"through":[145],"multiple":[146],"iterations.":[148],"Clustering":[149],"change":[153],"dataset":[155,176],"distribution":[156],"balance":[158],"number":[160],"positive":[162],"negative":[164],"samples.":[165],"in":[168],"paper":[170],"evaluated":[172],"public":[175],"Sisfall.":[177],"Compared":[178],"traditional":[181],"algorithms,":[184],"has":[188,203],"higher":[189,205],"faster":[192],"speed.":[194],"Combined":[195],"method,":[200],"recall":[206],"precision.":[208]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2022-09-06T00:00:00"}
