{"id":"https://openalex.org/W4313132639","doi":"https://doi.org/10.1109/access.2022.3221425","title":"Sensor-Based Open-Set Human Activity Recognition Using Representation Learning With Mixup Triplets","display_name":"Sensor-Based Open-Set Human Activity Recognition Using Representation Learning With Mixup Triplets","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4313132639","doi":"https://doi.org/10.1109/access.2022.3221425"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3221425","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3221425","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09945967.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09945967.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101652437","display_name":"Minjung Lee","orcid":"https://orcid.org/0000-0002-7110-7353"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Minjung Lee","raw_affiliation_strings":["School of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-7110-7353","affiliations":[{"raw_affiliation_string":"School of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058258354","display_name":"Seoung Bum Kim","orcid":"https://orcid.org/0000-0002-2205-8516"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seoung Bum Kim","raw_affiliation_strings":["School of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-2205-8516","affiliations":[{"raw_affiliation_string":"School of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6852,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.70165903,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"10","issue":null,"first_page":"119333","last_page":"119344"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9922999739646912,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9865999817848206,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.776301383972168},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6310318112373352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6268938183784485},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6043174862861633},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5654564499855042},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.5643231272697449},{"id":"https://openalex.org/keywords/open-set","display_name":"Open set","score":0.547786295413971},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5042747259140015},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46699395775794983},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4549919068813324},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4486421048641205},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4359324872493744},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4354787766933441},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1059524416923523}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.776301383972168},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6310318112373352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6268938183784485},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6043174862861633},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5654564499855042},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.5643231272697449},{"id":"https://openalex.org/C42357961","wikidata":"https://www.wikidata.org/wiki/Q213363","display_name":"Open set","level":2,"score":0.547786295413971},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5042747259140015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46699395775794983},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4549919068813324},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4486421048641205},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4359324872493744},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4354787766933441},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1059524416923523},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3221425","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3221425","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09945967.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:369d9cda32864d41bc469a0f12fb5d19","is_oa":true,"landing_page_url":"https://doaj.org/article/369d9cda32864d41bc469a0f12fb5d19","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 119333-119344 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3221425","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3221425","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09945967.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[{"id":"https://openalex.org/G1698445836","display_name":null,"funder_award_id":"RS-2022-00144190","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G2959606156","display_name":null,"funder_award_id":"IITP-2020-0-01749","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G4557667495","display_name":null,"funder_award_id":"IITP-2020-0-01749","funder_id":"https://openalex.org/F4320324891","funder_display_name":"Iran Telecommunication Research Center"},{"id":"https://openalex.org/G4700831490","display_name":null,"funder_award_id":"2022-","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G7845272108","display_name":null,"funder_award_id":"IITP-2020-0-01749","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G8191624576","display_name":null,"funder_award_id":"RS-2022-00144190","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G8595406695","display_name":null,"funder_award_id":"UI2100062D","funder_id":"https://openalex.org/F4320323103","funder_display_name":"Agency for Defense Development"},{"id":"https://openalex.org/G961783548","display_name":null,"funder_award_id":"2020-0-01749","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320323103","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85"},{"id":"https://openalex.org/F4320324891","display_name":"Iran Telecommunication Research Center","ror":"https://ror.org/01a3g2z22"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4313132639.pdf","grobid_xml":"https://content.openalex.org/works/W4313132639.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W134960717","https://openalex.org/W1522301498","https://openalex.org/W1996118086","https://openalex.org/W2026297770","https://openalex.org/W2096733369","https://openalex.org/W2145343602","https://openalex.org/W2157364932","https://openalex.org/W2270470215","https://openalex.org/W2288074780","https://openalex.org/W2320625002","https://openalex.org/W2626967530","https://openalex.org/W2759690896","https://openalex.org/W2765407302","https://openalex.org/W2794717185","https://openalex.org/W2887770386","https://openalex.org/W2897510122","https://openalex.org/W2946502108","https://openalex.org/W2963149653","https://openalex.org/W2963314614","https://openalex.org/W2963664762","https://openalex.org/W2965144482","https://openalex.org/W2968356384","https://openalex.org/W2998376881","https://openalex.org/W3001316388","https://openalex.org/W3011785450","https://openalex.org/W3012698329","https://openalex.org/W3012760824","https://openalex.org/W3015014633","https://openalex.org/W3018953709","https://openalex.org/W3113123476","https://openalex.org/W3118707261","https://openalex.org/W3135100418","https://openalex.org/W3164954617","https://openalex.org/W3166187222","https://openalex.org/W3214801461","https://openalex.org/W4298861651","https://openalex.org/W6605479355","https://openalex.org/W6631190155","https://openalex.org/W6696429117","https://openalex.org/W6739651123","https://openalex.org/W6745136726","https://openalex.org/W6772938244","https://openalex.org/W6775189568"],"related_works":["https://openalex.org/W4382795578","https://openalex.org/W2355463328","https://openalex.org/W1431147547","https://openalex.org/W2402648945","https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353"],"abstract_inverted_index":{"The":[0,170],"main":[1],"objective":[2],"of":[3,61,73,77,83,120,144,160,189,192],"sensor-based":[4,166],"human":[5,13],"activity":[6,147],"recognition":[7],"(HAR)":[8],"is":[9,29,44,68],"to":[10,46,55,58],"classify":[11],"predefined":[12],"physical":[14],"activities":[15,35],"with":[16,109,131],"multi-channel":[17],"signals":[18],"acquired":[19],"from":[20,123,141],"wearable":[21],"sensors.":[22],"In":[23,101,126],"a":[24,69,105],"real-world":[25],"scenario,":[26],"signal":[27],"data":[28],"changing":[30],"over":[31],"time":[32],"and":[33,79,196],"undefined":[34],"may":[36],"occur.":[37],"Thus,":[38],"an":[39,50],"open-set":[40,66,99,153,184],"classifier":[41],"for":[42,94,98,150,182],"HAR":[43,67,167],"required":[45],"detect":[47],"it":[48,57],"as":[49],"unknown":[51],"class":[52],"rather":[53],"than":[54],"assign":[56],"the":[59,62,74,80,121,124,127,132,142,151,158,175,179],"one":[60],"known":[63,122,146],"classes.":[64],"However,":[65],"challenging":[70],"task":[71],"because":[72],"small":[75],"variability":[76,82],"inter-activities":[78],"large":[81],"intra-activities.":[84],"To":[85,156],"tackle":[86],"this":[87],"problem,":[88],"we":[89,103,162],"propose":[90],"mixup":[91],"triplet":[92],"learning":[93,108],"Mahalanobis":[95,139],"distance":[96],"(MTMD)":[97],"recognition.":[100,155],"MTMD,":[102,161],"introduce":[104],"new":[106],"representation":[107,134],"stochastically":[110],"linearly":[111],"interpolated":[112],"triplets":[113],"(mixup":[114],"triplets)":[115],"that":[116,174],"makes":[117],"discriminative":[118],"features":[119],"unknown.":[125],"trained":[128],"embedding":[129],"space":[130],"proposed":[133,152,176],"learning,":[135],"thresholds":[136],"based":[137],"on":[138,165],"distances":[140],"centers":[143],"each":[145],"are":[148],"defined":[149],"action":[154],"demonstrate":[157],"effectiveness":[159],"conducted":[163],"experiments":[164],"benchmark":[168],"datasets.":[169],"experimental":[171,185],"results":[172,191],"show":[173],"MTMD":[177],"outperforms":[178],"existing":[180],"methods":[181],"various":[183],"settings":[186],"in":[187],"terms":[188],"numerical":[190],"F1,":[193],"recall,":[194],"precision,":[195],"accuracy.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}