{"id":"https://openalex.org/W2964302218","doi":"https://doi.org/10.1145/3123021.3123054","title":"Unsupervised motion artifact detection in wrist-measured electrodermal activity data","display_name":"Unsupervised motion artifact detection in wrist-measured electrodermal activity data","publication_year":2017,"publication_date":"2017-09-07","ids":{"openalex":"https://openalex.org/W2964302218","doi":"https://doi.org/10.1145/3123021.3123054","mag":"2964302218"},"language":"en","primary_location":{"id":"doi:10.1145/3123021.3123054","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3123021.3123054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM International Symposium on Wearable Computers","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/A5100687525","display_name":"Yuning Zhang","orcid":"https://orcid.org/0000-0002-1595-5067"},"institutions":[{"id":"https://openalex.org/I90871651","display_name":"University of Toledo","ror":"https://ror.org/01pbdzh19","country_code":"US","type":"education","lineage":["https://openalex.org/I90871651"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuning Zhang","raw_affiliation_strings":["University of Toledo"],"affiliations":[{"raw_affiliation_string":"University of Toledo","institution_ids":["https://openalex.org/I90871651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003828614","display_name":"Maysam Haghdan","orcid":null},"institutions":[{"id":"https://openalex.org/I90871651","display_name":"University of Toledo","ror":"https://ror.org/01pbdzh19","country_code":"US","type":"education","lineage":["https://openalex.org/I90871651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maysam Haghdan","raw_affiliation_strings":["University of Toledo"],"affiliations":[{"raw_affiliation_string":"University of Toledo","institution_ids":["https://openalex.org/I90871651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009765913","display_name":"Kevin S. Xu","orcid":"https://orcid.org/0000-0002-2029-5905"},"institutions":[{"id":"https://openalex.org/I90871651","display_name":"University of Toledo","ror":"https://ror.org/01pbdzh19","country_code":"US","type":"education","lineage":["https://openalex.org/I90871651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin S. Xu","raw_affiliation_strings":["University of Toledo"],"affiliations":[{"raw_affiliation_string":"University of Toledo","institution_ids":["https://openalex.org/I90871651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100687525"],"corresponding_institution_ids":["https://openalex.org/I90871651"],"apc_list":null,"apc_paid":null,"fwci":1.6661,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.84176022,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"54","last_page":"57"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9976999759674072,"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"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9976999759674072,"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"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9951000213623047,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7621290683746338},{"id":"https://openalex.org/keywords/artifact","display_name":"Artifact (error)","score":0.6917786598205566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6889609098434448},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6609088182449341},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.6580708026885986},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.6477272510528564},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5475255250930786},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.50995934009552},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5067523121833801},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.48938053846359253},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.457346647977829},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4332549273967743},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.4140954911708832},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4055480659008026},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1946696639060974}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7621290683746338},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.6917786598205566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6889609098434448},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6609088182449341},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.6580708026885986},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6477272510528564},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5475255250930786},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.50995934009552},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5067523121833801},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.48938053846359253},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.457346647977829},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4332549273967743},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.4140954911708832},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4055480659008026},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1946696639060974},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3123021.3123054","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3123021.3123054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM International Symposium on Wearable Computers","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":8,"referenced_works":["https://openalex.org/W2054780155","https://openalex.org/W2062914254","https://openalex.org/W2105036571","https://openalex.org/W2155961734","https://openalex.org/W2294683276","https://openalex.org/W2295748404","https://openalex.org/W2532518856","https://openalex.org/W2563752936"],"related_works":["https://openalex.org/W4285260836","https://openalex.org/W3208099188","https://openalex.org/W3046775127","https://openalex.org/W3094076422","https://openalex.org/W4283732135","https://openalex.org/W4319309271","https://openalex.org/W3186093657","https://openalex.org/W3199974879","https://openalex.org/W3162567751","https://openalex.org/W3192794374"],"abstract_inverted_index":{"One":[0],"of":[1,5,16,68,138,166],"the":[2,62,66,72,164],"main":[3],"benefits":[4],"a":[6,14,20,35,38,48,95,101,127,131],"wrist-worn":[7],"computer":[8],"is":[9,30],"its":[10],"ability":[11],"to":[12],"collect":[13],"variety":[15],"physiological":[17],"data":[18,45,63,73,97,123,148],"in":[19,100,125],"minimally":[21],"intrusive":[22],"manner.":[23],"Among":[24],"these":[25],"data,":[26],"electrodermal":[27],"activity":[28],"(EDA)":[29],"readily":[31],"collected":[32,46,99,124],"and":[33,41,64,77,130,161],"provides":[34],"window":[36],"into":[37],"person's":[39],"emotional":[40],"sympathetic":[42],"responses.":[43],"EDA":[44,122,152],"using":[47,89],"wearable":[49],"wristband":[50],"are":[51],"easily":[52],"influenced":[53],"by":[54],"motion":[55],"artifacts":[56],"(MAs)":[57],"that":[58,83,109,145],"may":[59],"significantly":[60,162],"distort":[61],"degrade":[65],"quality":[67],"analyses":[69],"performed":[70],"on":[71,94,121],"if":[74],"not":[75],"identified":[76],"removed.":[78],"Prior":[79],"work":[80],"has":[81],"demonstrated":[82],"MAs":[84,120],"can":[85],"be":[86],"successfully":[87],"detected":[88],"supervised":[90,116,159],"machine":[91],"learning":[92,111],"algorithms":[93,112,117,160],"small":[96],"set":[98],"lab":[102],"setting.":[103],"In":[104],"this":[105],"paper,":[106],"we":[107],"demonstrate":[108],"unsupervised":[110,167],"perform":[113],"competitively":[114],"with":[115],"for":[118,158],"detecting":[119],"both":[126],"lab-based":[128],"setting":[129,133],"real-world":[132],"comprising":[134],"about":[135],"23":[136],"hours":[137],"data.":[139],"We":[140],"also":[141],"find,":[142],"somewhat":[143],"surprisingly,":[144],"incorporating":[146],"accelerometer":[147],"as":[149,151],"well":[150],"improves":[153],"detection":[154],"accuracy":[155,165],"only":[156],"slightly":[157],"degrades":[163],"algorithms.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}