{"id":"https://openalex.org/W2996143655","doi":"https://doi.org/10.1109/aciiw.2019.8925192","title":"Real-time pain detection in facial expressions for health robotics","display_name":"Real-time pain detection in facial expressions for health robotics","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2996143655","doi":"https://doi.org/10.1109/aciiw.2019.8925192","mag":"2996143655"},"language":"en","primary_location":{"id":"doi:10.1109/aciiw.2019.8925192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aciiw.2019.8925192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","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/A5081991869","display_name":"Laduona Dai","orcid":"https://orcid.org/0000-0002-1566-5764"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Laduona Dai","raw_affiliation_strings":["University of Twente Human Media Interaction, Enschede, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Twente Human Media Interaction, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027253360","display_name":"Joost Broekens","orcid":"https://orcid.org/0000-0001-9198-898X"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Joost Broekens","raw_affiliation_strings":["LIACS Leiden University, Leiden, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIACS Leiden University, Leiden, Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068294985","display_name":"Khiet P. Truong","orcid":"https://orcid.org/0000-0002-7243-0523"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Khiet P. Truong","raw_affiliation_strings":["Human Media Interaction University of Twente, Enschede, Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Human Media Interaction University of Twente, Enschede, Netherlands","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0135,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.79613024,"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":"277","last_page":"283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9984999895095825,"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":0.9984999895095825,"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/T11292","display_name":"Pediatric Pain Management Techniques","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T12035","display_name":"Pain Management and Placebo Effect","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7887477278709412},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7755081653594971},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6748702526092529},{"id":"https://openalex.org/keywords/humanoid-robot","display_name":"Humanoid robot","score":0.6453032493591309},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.5939697027206421},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5262051224708557},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.44702625274658203},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4408068060874939},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.35629090666770935}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7887477278709412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7755081653594971},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6748702526092529},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.6453032493591309},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.5939697027206421},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5262051224708557},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.44702625274658203},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4408068060874939},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.35629090666770935}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/aciiw.2019.8925192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aciiw.2019.8925192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","raw_type":"proceedings-article"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/b75927e3-a70e-43eb-b06d-b4f2ffb4c340","is_oa":false,"landing_page_url":"https://research.utwente.nl/en/publications/b75927e3-a70e-43eb-b06d-b4f2ffb4c340","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Dai, L, Broekens, J & Truong, K P 2019, Real-time pain detection in facial expressions for health robotics. in 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)., 8925192, IEEE, Piscataway, NJ, pp. 277-283, 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, Cambridge, United Kingdom, 3/09/19. https://doi.org/10.1109/ACIIW.2019.8925192","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1576320723","https://openalex.org/W1628791547","https://openalex.org/W1970680563","https://openalex.org/W1985941926","https://openalex.org/W2041616772","https://openalex.org/W2056277131","https://openalex.org/W2085156029","https://openalex.org/W2094821430","https://openalex.org/W2099093791","https://openalex.org/W2101545465","https://openalex.org/W2106043670","https://openalex.org/W2113585314","https://openalex.org/W2127113745","https://openalex.org/W2162770979","https://openalex.org/W2194775991","https://openalex.org/W2364221285","https://openalex.org/W2399802263","https://openalex.org/W2463259203","https://openalex.org/W2537075127","https://openalex.org/W2587128043","https://openalex.org/W2612445135","https://openalex.org/W2744909235","https://openalex.org/W2807126412","https://openalex.org/W2912527805","https://openalex.org/W4233212795","https://openalex.org/W4297775537","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W2745063183","https://openalex.org/W4256317079","https://openalex.org/W2129850190","https://openalex.org/W2295425790","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Automatic":[0],"pain":[1,23,93,102,121,155,187],"detection":[2,24,156],"is":[3,48,65,149],"an":[4,113],"important":[5],"challenge":[6],"in":[7,56,69,139],"health":[8],"computing.":[9],"In":[10,84],"this":[11,34,70,85],"paper":[12],"we":[13,87,152],"report":[14],"on":[15,61,76,117,158],"our":[16,154],"efforts":[17],"to":[18,58],"develop":[19],"a":[20,99,118,159,181],"real-time,":[21],"real-world":[22,82],"system":[25],"from":[26],"human":[27],"facial":[28],"expressions.":[29,126],"Although":[30],"many":[31],"studies":[32],"addressed":[33],"challenge,":[35],"most":[36],"of":[37,91,101,115,169],"them":[38],"use":[39],"the":[40,73,136,147,167,178],"same":[41],"dataset":[42,119,148],"for":[43,162,180],"training":[44,96],"and":[45,103,122,184],"testing.":[46],"There":[47],"no":[49],"cross-check":[50],"with":[51],"other":[52,107],"datasets":[53],"or":[54],"implementation":[55],"real-time":[57,92,173],"check":[59],"performance":[60],"new":[62],"data.":[63],"This":[64,79],"problematic,":[66],"as":[67,142,175,177],"evidenced":[68],"paper,":[71,86],"because":[72],"classifiers":[74],"overtrain":[75,145],"dataset-specific":[77],"features.":[78],"limits":[80],"realtime,":[81],"usage.":[83],"investigate":[88],"different":[89],"methods":[90,138],"detection.":[94],"The":[95,109],"data":[97],"uses":[98],"combination":[100],"emotion":[104],"datasets,":[105],"unlike":[106],"papers.":[108],"best":[110,137],"model":[111],"shows":[112],"accuracy":[114],"88.4%":[116],"including":[120],"7":[123],"non-pain":[124],"emotional":[125],"Results":[127],"suggest":[128],"that":[129],"convolutional":[130],"neural":[131],"networks":[132],"(CNN)":[133],"are":[134],"not":[135],"some":[140],"cases":[141],"they":[143],"easily":[144],"if":[146],"biased.":[150],"Finally":[151],"implemented":[153],"method":[157],"humanoid":[160],"robot":[161],"physiotherapy.":[163],"Our":[164],"work":[165],"highlights":[166],"importance":[168],"cross-corpus":[170],"evaluation":[171],"&":[172],"testing,":[174],"well":[176,182],"need":[179],"balanced":[183],"ecologically":[185],"valid":[186],"dataset.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
