{"id":"https://openalex.org/W2913404518","doi":"https://doi.org/10.1109/ssci.2018.8628695","title":"Classification of Physiological Data in Affective Exergames","display_name":"Classification of Physiological Data in Affective Exergames","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2913404518","doi":"https://doi.org/10.1109/ssci.2018.8628695","mag":"2913404518"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2018.8628695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2018.8628695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5000004223","display_name":"Andreas Kamenz","orcid":null},"institutions":[{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Andreas Kamenz","raw_affiliation_strings":["HAW Hamburg, Informatik Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"HAW Hamburg, Informatik Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001296385","display_name":"Victoria Bibaeva","orcid":null},"institutions":[{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Victoria Bibaeva","raw_affiliation_strings":["HAW Hamburg, Informatik Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"HAW Hamburg, Informatik Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085207486","display_name":"Arne Bernin","orcid":null},"institutions":[{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Arne Bernin","raw_affiliation_strings":["HAW Hamburg, University of the West of Scotland (UWS), Germany"],"affiliations":[{"raw_affiliation_string":"HAW Hamburg, University of the West of Scotland (UWS), Germany","institution_ids":["https://openalex.org/I70451448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068911191","display_name":"Sobin Ghose","orcid":null},"institutions":[{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sobin Ghose","raw_affiliation_strings":["HAW Hamburg, Informatik Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"HAW Hamburg, Informatik Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031878637","display_name":"Kai von Luck","orcid":"https://orcid.org/0000-0002-5177-3025"},"institutions":[{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kai von Luck","raw_affiliation_strings":["HAW Hamburg, Informatik Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"HAW Hamburg, Informatik Hamburg, Germany","institution_ids":["https://openalex.org/I70451448"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071258464","display_name":"Florian Vogt","orcid":"https://orcid.org/0000-0003-2686-3149"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Florian Vogt","raw_affiliation_strings":["Innovations Kontakt Stelle (IKS), Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"Innovations Kontakt Stelle (IKS), Hamburg, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003286379","display_name":"Larissa M\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Larissa Muller","raw_affiliation_strings":["PricewaterhouseCoopers GmbH WPG, Hamburg, Germany"],"affiliations":[{"raw_affiliation_string":"PricewaterhouseCoopers GmbH WPG, Hamburg, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5000004223"],"corresponding_institution_ids":["https://openalex.org/I70451448"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.18404827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2076","last_page":"2081"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.994700014591217,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9896000027656555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.756493330001831},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6217948198318481},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5836544036865234},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5805977582931519},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5519843697547913},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5366107821464539},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4841471314430237},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.46390148997306824},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.46028321981430054},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.44192326068878174},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.44153285026550293},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.429576575756073},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2955171465873718},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.21876096725463867},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09058114886283875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.756493330001831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6217948198318481},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5836544036865234},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5805977582931519},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5519843697547913},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5366107821464539},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4841471314430237},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.46390148997306824},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.46028321981430054},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.44192326068878174},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.44153285026550293},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.429576575756073},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2955171465873718},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.21876096725463867},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09058114886283875},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci.2018.8628695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2018.8628695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W276943633","https://openalex.org/W568767537","https://openalex.org/W1765835586","https://openalex.org/W2067904803","https://openalex.org/W2090216488","https://openalex.org/W2113404160","https://openalex.org/W2155891138","https://openalex.org/W2156387975","https://openalex.org/W2161725019","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2545001194","https://openalex.org/W2548648497","https://openalex.org/W2551393996","https://openalex.org/W2555077524","https://openalex.org/W2566338991","https://openalex.org/W2588483054","https://openalex.org/W2755241981","https://openalex.org/W2786085966","https://openalex.org/W2786410355","https://openalex.org/W2787319633","https://openalex.org/W2787868933","https://openalex.org/W3104797784","https://openalex.org/W3141077511","https://openalex.org/W4240121866","https://openalex.org/W4251708881","https://openalex.org/W4285719527","https://openalex.org/W6667331557","https://openalex.org/W6676695001","https://openalex.org/W6682889407","https://openalex.org/W6684191040","https://openalex.org/W6729149246"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2523437662","https://openalex.org/W89844371","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456","https://openalex.org/W2136232598"],"abstract_inverted_index":{"In":[0,19,60],"this":[1],"work,":[2],"we":[3,22,38,68],"present":[4],"our":[5,103],"approach":[6,74],"to":[7,28,57,62,98],"analyze":[8,63],"physiological":[9,43,66],"data":[10,44],"in":[11],"affective":[12],"exergames":[13],"by":[14],"using":[15],"deep":[16,79],"learning":[17,73,80],"algorithms.":[18],"previous":[20],"works,":[21],"enhanced":[23],"a":[24,31,35,51,91],"cycling":[25],"exercise":[26],"machine":[27],"act":[29],"as":[30],"game":[32,52],"controller.":[33],"During":[34],"case":[36],"study,":[37],"then":[39],"collected":[40,65],"vision-based":[41],"and":[42,87],"of":[45,102],"25":[46],"participants":[47],"who":[48],"rode":[49],"through":[50],"environment":[53],"that":[54],"was":[55],"designed":[56],"provoke":[58],"emotions.":[59],"order":[61],"the":[64,93,100],"data,":[67],"now":[69],"propose":[70],"an":[71],"ensemble":[72],"based":[75],"on":[76],"three":[77],"distinct":[78],"models:":[81],"Multilayer":[82],"Perceptron,":[83],"Fully":[84],"Convolutional":[85],"Networks":[86],"Residual":[88],"Networks.":[89],"As":[90],"result,":[92],"proposed":[94],"algorithms":[95],"were":[96],"able":[97],"enhance":[99],"quality":[101],"event-based":[104],"emotion":[105],"analysis":[106],"method":[107],"introduced":[108],"previously.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}