{"id":"https://openalex.org/W2732997537","doi":"https://doi.org/10.1109/cec.2017.7969569","title":"Feature selection for physical activity recognition using genetic algorithms","display_name":"Feature selection for physical activity recognition using genetic algorithms","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2732997537","doi":"https://doi.org/10.1109/cec.2017.7969569","mag":"2732997537"},"language":"en","primary_location":{"id":"doi:10.1109/cec.2017.7969569","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2017.7969569","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Congress on Evolutionary Computation (CEC)","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/A5069993099","display_name":"Alejandro Baldominos","orcid":"https://orcid.org/0000-0002-8906-7572"},"institutions":[{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]},{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES","FR"],"is_corresponding":false,"raw_author_name":"Alejandro Baldominos","raw_affiliation_strings":["Computer Science Department, Universidad Carlos III de Madrid"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Universidad Carlos III de Madrid","institution_ids":["https://openalex.org/I50357001","https://openalex.org/I4210156583"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114585209","display_name":"Pedro Isasi","orcid":"https://orcid.org/0000-0001-6397-1865"},"institutions":[{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]},{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES","FR"],"is_corresponding":false,"raw_author_name":"Pedro Isasi","raw_affiliation_strings":["Computer Science Department, Universidad Carlos III de Madrid"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Universidad Carlos III de Madrid","institution_ids":["https://openalex.org/I50357001","https://openalex.org/I4210156583"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027117264","display_name":"Yago S\u00e1ez","orcid":"https://orcid.org/0000-0002-0998-2907"},"institutions":[{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]},{"id":"https://openalex.org/I50357001","display_name":"Universidad Carlos III de Madrid","ror":"https://ror.org/03ths8210","country_code":"ES","type":"education","lineage":["https://openalex.org/I50357001"]}],"countries":["ES","FR"],"is_corresponding":false,"raw_author_name":"Yago Saez","raw_affiliation_strings":["Computer Science Department, Universidad Carlos III de Madrid"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Universidad Carlos III de Madrid","institution_ids":["https://openalex.org/I50357001","https://openalex.org/I4210156583"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6466,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.78393562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2185","last_page":"2192"},"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/T10352","display_name":"Physical Activity and Health","score":0.9768000245094299,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11446","display_name":"Mobile Health and mHealth Applications","score":0.9718000292778015,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"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":0.7228941321372986},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.7220470309257507},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7042755484580994},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6688669323921204},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6379579305648804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6179848313331604},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6048742532730103},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5515689849853516},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4934019148349762},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.42573487758636475},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.41582590341567993},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40317919850349426},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36766019463539124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7228941321372986},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.7220470309257507},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7042755484580994},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6688669323921204},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6379579305648804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6179848313331604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6048742532730103},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5515689849853516},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4934019148349762},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.42573487758636475},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.41582590341567993},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40317919850349426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36766019463539124}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec.2017.7969569","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec.2017.7969569","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W166825140","https://openalex.org/W176323845","https://openalex.org/W1937596112","https://openalex.org/W1966437566","https://openalex.org/W1968670561","https://openalex.org/W1973221871","https://openalex.org/W1979061557","https://openalex.org/W1995784006","https://openalex.org/W1999712889","https://openalex.org/W2009143818","https://openalex.org/W2011458502","https://openalex.org/W2012557818","https://openalex.org/W2023302299","https://openalex.org/W2026297770","https://openalex.org/W2030980398","https://openalex.org/W2031461964","https://openalex.org/W2033938404","https://openalex.org/W2038083678","https://openalex.org/W2039479608","https://openalex.org/W2041557010","https://openalex.org/W2054242744","https://openalex.org/W2054780155","https://openalex.org/W2056132907","https://openalex.org/W2062880872","https://openalex.org/W2063598276","https://openalex.org/W2066856358","https://openalex.org/W2079481888","https://openalex.org/W2081135661","https://openalex.org/W2082450394","https://openalex.org/W2094236633","https://openalex.org/W2096823672","https://openalex.org/W2099864164","https://openalex.org/W2129343065","https://openalex.org/W2132025349","https://openalex.org/W2137035460","https://openalex.org/W2142330515","https://openalex.org/W2145702257","https://openalex.org/W2146476355","https://openalex.org/W2147424594","https://openalex.org/W2270422077","https://openalex.org/W2270470215","https://openalex.org/W2330981329","https://openalex.org/W2396993973","https://openalex.org/W2506886870","https://openalex.org/W2568838907","https://openalex.org/W2578252685","https://openalex.org/W2761795154","https://openalex.org/W6671174334","https://openalex.org/W6674946927","https://openalex.org/W6681490269"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021","https://openalex.org/W2389417819"],"abstract_inverted_index":{"Physical":[0],"activity":[1,47,60,88,99,108],"is":[2,48,50,63,111,130],"widely":[3],"known":[4],"to":[5,74,85,102,153,184],"be":[6],"a":[7,12,61,97,104,117,126,186],"key":[8],"factor":[9],"towards":[10],"achieving":[11],"healthy":[13],"life":[14],"and":[15,66,136,142,157,168,171,188],"reducing":[16],"the":[17,58,87,155,159,192],"chance":[18],"of":[19,45,90,161,174,204],"developing":[20],"certain":[21],"diseases.":[22],"However,":[23],"there":[24],"are":[25],"many":[26],"different":[27,31,36,144],"physical":[28,46,59,107],"activities":[29],"having":[30,35],"effort":[32],"requirements":[33],"or":[34],"benefits":[37],"on":[38,69],"health.":[39],"The":[40],"reason":[41],"why":[42],"automatic":[43],"recognition":[44,100],"useful":[49],"twofold:":[51],"first,":[52],"it":[53,81],"raises":[54],"personal":[55],"awareness":[56],"about":[57],"user":[62],"carrying":[64],"out":[65],"its":[67],"impact":[68],"health,":[70],"allowing":[71],"some":[72],"apps":[73],"give":[75],"proper":[76],"credit":[77],"for":[78,106],"it;":[79],"second,":[80],"allows":[82],"medical":[83],"staff":[84],"monitor":[86],"levels":[89],"patients.":[91],"In":[92],"this":[93],"paper,":[94],"we":[95,139,180,189],"follow":[96],"proven":[98],"chain":[101],"learn":[103],"classifier":[105],"recognition,":[109],"which":[110],"trained":[112],"using":[113,148],"data":[114],"from":[115],"PAMAP2,":[116],"dataset":[118,129],"publicly":[119],"available":[120],"in":[121,151,198],"UCI":[122],"ML":[123],"repository.":[124],"Once":[125],"machine":[127],"learning":[128],"created":[131],"after":[132],"signal":[133],"preprocessing,":[134],"segmentation":[135],"feature":[137,145],"extraction,":[138],"will":[140],"explore":[141],"compare":[143],"selection":[146],"techniques":[147],"genetic":[149],"algorithms":[150],"order":[152],"maximize":[154],"accuracy":[156,203],"reduce":[158],"number":[160],"dimensions.":[162],"This":[163],"reduction":[164],"improves":[165],"classification":[166],"times":[167],"reduces":[169],"costs":[170],"energy":[172],"consumption":[173],"sensor":[175],"devices.":[176],"By":[177],"doing":[178],"so,":[179],"have":[181,190],"reduced":[182],"dimensions":[183],"almost":[185],"half":[187],"outperformed":[191],"best":[193],"results":[194],"found":[195],"so":[196],"far":[197],"literature":[199],"with":[200],"an":[201],"average":[202],"97.45%.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}