{"id":"https://openalex.org/W2143474525","doi":"https://doi.org/10.1142/s0218001412500085","title":"STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING","display_name":"STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2143474525","doi":"https://doi.org/10.1142/s0218001412500085","mag":"2143474525"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001412500085","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001412500085","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-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/A5111803378","display_name":"Tung-Hung Chueh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148468","display_name":"Industrial Technology Research Institute","ror":"https://ror.org/05szzwt63","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I4210148468"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"TUNG-HUNG CHUEH","raw_affiliation_strings":["Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Chutung, Hsinchu 310, Taiwan, R. O. C"],"affiliations":[{"raw_affiliation_string":"Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, Chutung, Hsinchu 310, Taiwan, R. O. C","institution_ids":["https://openalex.org/I4210148468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063161766","display_name":"Tai\u2010Been Chen","orcid":"https://orcid.org/0000-0002-3348-4422"},"institutions":[{"id":"https://openalex.org/I152743029","display_name":"Kaohsiung Medical University","ror":"https://ror.org/03gk81f96","country_code":"TW","type":"education","lineage":["https://openalex.org/I152743029"]},{"id":"https://openalex.org/I98298690","display_name":"I-Shou University","ror":"https://ror.org/04d7e4m76","country_code":"TW","type":"education","lineage":["https://openalex.org/I98298690"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"TAI-BEEN CHEN","raw_affiliation_strings":["Department of Medical Imaging and Radiological Sciences, I-Shou University Kaohsiung 824, Taiwan, R. O. C"],"affiliations":[{"raw_affiliation_string":"Department of Medical Imaging and Radiological Sciences, I-Shou University Kaohsiung 824, Taiwan, R. O. C","institution_ids":["https://openalex.org/I98298690","https://openalex.org/I152743029"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052673069","display_name":"Henry Horng\u2010Shing Lu","orcid":"https://orcid.org/0000-0002-4392-3361"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"HENRY HORNG-SHING LU","raw_affiliation_strings":["Institute of Statistics, National Chiao Tung University, Hsinchu 300, Taiwan, R. O. C"],"affiliations":[{"raw_affiliation_string":"Institute of Statistics, National Chiao Tung University, Hsinchu 300, Taiwan, R. O. C","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108227370","display_name":"SHAN-SHAN JU","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148468","display_name":"Industrial Technology Research Institute","ror":"https://ror.org/05szzwt63","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I4210148468"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"SHAN-SHAN JU","raw_affiliation_strings":["Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C"],"affiliations":[{"raw_affiliation_string":"Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C","institution_ids":["https://openalex.org/I4210148468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002901554","display_name":"Teh-Ho Tao","orcid":"https://orcid.org/0000-0002-6822-5316"},"institutions":[{"id":"https://openalex.org/I4210148468","display_name":"Industrial Technology Research Institute","ror":"https://ror.org/05szzwt63","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I4210148468"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"TEH-HO TAO","raw_affiliation_strings":["Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C"],"affiliations":[{"raw_affiliation_string":"Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C","institution_ids":["https://openalex.org/I4210148468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112011735","display_name":"Jiunn-Haur Shaw","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148468","display_name":"Industrial Technology Research Institute","ror":"https://ror.org/05szzwt63","country_code":"TW","type":"nonprofit","lineage":["https://openalex.org/I4210148468"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"JIUNN-HAUR SHAW","raw_affiliation_strings":["Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C"],"affiliations":[{"raw_affiliation_string":"Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 300, Taiwan, R. O. C","institution_ids":["https://openalex.org/I4210148468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5111803378"],"corresponding_institution_ids":["https://openalex.org/I4210148468"],"apc_list":null,"apc_paid":null,"fwci":0.5446,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.74349234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"26","issue":"04","first_page":"1250008","last_page":"1250008"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.989300012588501,"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/T12496","display_name":"Color perception and design","score":0.9684000015258789,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.721381425857544},{"id":"https://openalex.org/keywords/multivariate-analysis-of-variance","display_name":"Multivariate analysis of variance","score":0.6570039391517639},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6383157968521118},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6355867385864258},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6267024278640747},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6251858472824097},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5432804822921753},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4632180333137512},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4616374671459198},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.45502620935440063},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4491075873374939},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44565701484680176},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4295973777770996}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.721381425857544},{"id":"https://openalex.org/C192424360","wikidata":"https://www.wikidata.org/wiki/Q929408","display_name":"Multivariate analysis of variance","level":2,"score":0.6570039391517639},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6383157968521118},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6355867385864258},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6267024278640747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6251858472824097},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5432804822921753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4632180333137512},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4616374671459198},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.45502620935440063},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4491075873374939},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44565701484680176},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4295973777770996}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001412500085","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001412500085","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1504694836","https://openalex.org/W1580783489","https://openalex.org/W1593793857","https://openalex.org/W1755360231","https://openalex.org/W1912123407","https://openalex.org/W1923034539","https://openalex.org/W2025175857","https://openalex.org/W2032254851","https://openalex.org/W2033773055","https://openalex.org/W2063412092","https://openalex.org/W2064149108","https://openalex.org/W2074584355","https://openalex.org/W2078671978","https://openalex.org/W2100860548","https://openalex.org/W2106393550","https://openalex.org/W2110683152","https://openalex.org/W2111926505","https://openalex.org/W2119200170","https://openalex.org/W2120945046","https://openalex.org/W2125055259","https://openalex.org/W2143350951","https://openalex.org/W2147169507","https://openalex.org/W2148603752","https://openalex.org/W2149186291","https://openalex.org/W2151040995","https://openalex.org/W2159080219","https://openalex.org/W2168341643","https://openalex.org/W4247767684","https://openalex.org/W4254721730","https://openalex.org/W4299670631"],"related_works":["https://openalex.org/W4321636153","https://openalex.org/W3143658565","https://openalex.org/W4214820172","https://openalex.org/W4307730291","https://openalex.org/W4281846282","https://openalex.org/W4205958290","https://openalex.org/W4246246790","https://openalex.org/W3127425528","https://openalex.org/W4283016678","https://openalex.org/W4200196661"],"abstract_inverted_index":{"For":[0],"the":[1,20,37,44,56,83,90,120,130,135,143],"importance":[2],"of":[3,46,59,64,93,109,138,145],"communication":[4],"between":[5],"human":[6],"and":[7,40,49,76,86,113,134],"machine":[8,96,148],"interface,":[9],"it":[10],"would":[11],"be":[12],"valuable":[13],"to":[14,22,118],"develop":[15],"an":[16,31],"implement":[17],"which":[18,33],"has":[19],"ability":[21],"recognize":[23],"emotional":[24,121],"states.":[25,122],"In":[26],"this":[27],"paper,":[28],"we":[29],"proposed":[30],"approach":[32],"can":[34,140],"deal":[35],"with":[36],"daily":[38,84],"dependence":[39,42,85,88],"personal":[41,87],"in":[43,151],"data":[45],"multiple":[47],"subjects":[48],"samples.":[50],"30":[51],"features":[52],"were":[53,116],"extracted":[54],"from":[55],"physiological":[57,67],"signals":[58,68],"subject":[60],"for":[61],"three":[62],"states":[63],"emotion.":[65],"The":[66,123],"measured":[69],"were:":[70],"electrocardiogram":[71],"(ECG),":[72],"skin":[73,78],"temperature":[74],"(SKT)":[75],"galvanic":[77],"response":[79],"(GSR).":[80],"After":[81],"removing":[82],"by":[89],"statistical":[91,136],"technique":[92,137],"MANOVA,":[94],"six":[95,147],"learning":[97,149],"methods":[98,150],"including":[99],"Bayesian":[100,104],"network":[101],"learning,":[102],"naive":[103],"classification,":[105],"SVM,":[106],"decision":[107],"tree":[108],"C4.5,":[110],"Logistic":[111,127],"model":[112,128],"K-nearest-neighbor":[114],"(KNN)":[115],"implemented":[117],"differentiate":[119],"results":[124],"showed":[125],"that":[126],"gives":[129],"best":[131],"classification":[132],"accuracy":[133],"MANOVA":[139],"significantly":[141],"improve":[142],"performance":[144],"all":[146],"emotion":[152],"recognition":[153],"system.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
