{"id":"https://openalex.org/W3166187222","doi":"https://doi.org/10.1109/percom50583.2021.9439116","title":"Deep Triplet Networks with Attention for Sensor-based Human Activity Recognition","display_name":"Deep Triplet Networks with Attention for Sensor-based Human Activity Recognition","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3166187222","doi":"https://doi.org/10.1109/percom50583.2021.9439116","mag":"3166187222"},"language":"en","primary_location":{"id":"doi:10.1109/percom50583.2021.9439116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom50583.2021.9439116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)","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/A5086064165","display_name":"Bulat Khaertdinov","orcid":"https://orcid.org/0000-0003-1651-0657"},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Bulat Khaertdinov","raw_affiliation_strings":["Maastricht University, Maastricht, the Netherlands"],"affiliations":[{"raw_affiliation_string":"Maastricht University, Maastricht, the Netherlands","institution_ids":["https://openalex.org/I34352273"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046627420","display_name":"Esam Ghaleb","orcid":"https://orcid.org/0000-0002-0603-9817"},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Esam Ghaleb","raw_affiliation_strings":["Maastricht University, Maastricht, the Netherlands"],"affiliations":[{"raw_affiliation_string":"Maastricht University, Maastricht, the Netherlands","institution_ids":["https://openalex.org/I34352273"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040216921","display_name":"Stylianos Asteriadis","orcid":"https://orcid.org/0000-0002-4298-6870"},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Stylianos Asteriadis","raw_affiliation_strings":["Maastricht University, Maastricht, the Netherlands"],"affiliations":[{"raw_affiliation_string":"Maastricht University, Maastricht, the Netherlands","institution_ids":["https://openalex.org/I34352273"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5086064165"],"corresponding_institution_ids":["https://openalex.org/I34352273"],"apc_list":null,"apc_paid":null,"fwci":3.6506,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.94457516,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9889000058174133,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9605000019073486,"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":0.7897377014160156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7248255610466003},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.636532723903656},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6289181113243103},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6195180416107178},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6153978109359741},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5631316304206848},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5499153733253479},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5266159176826477},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36826393008232117},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07278996706008911}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7897377014160156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7248255610466003},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.636532723903656},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6289181113243103},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6195180416107178},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6153978109359741},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5631316304206848},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5499153733253479},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5266159176826477},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36826393008232117},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07278996706008911},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/percom50583.2021.9439116","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom50583.2021.9439116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"},{"id":"pmh:oai:cris.maastrichtuniversity.nl:openaire_cris_publications/18cb218b-0335-4b3b-9c27-afef60c61f48","is_oa":false,"landing_page_url":"https://cris.maastrichtuniversity.nl/en/publications/18cb218b-0335-4b3b-9c27-afef60c61f48","pdf_url":null,"source":{"id":"https://openalex.org/S4306402616","display_name":"Research Publications (Maastricht University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I34352273","host_organization_name":"Maastricht University","host_organization_lineage":["https://openalex.org/I34352273"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Khaertdinov, B, Ghaleb, E & Asteriadis, S 2021, Deep Triplet Networks with Attention for Sensor-based Human Activity Recognition. in 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)., 9439116, IEEE Xplore, pp. 1-10, 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom), 22/03/21. https://doi.org/10.1109/PERCOM50583.2021.9439116","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W123295786","https://openalex.org/W1522301498","https://openalex.org/W1852728451","https://openalex.org/W2012557818","https://openalex.org/W2023302299","https://openalex.org/W2026297770","https://openalex.org/W2064675550","https://openalex.org/W2096733369","https://openalex.org/W2138621090","https://openalex.org/W2145343602","https://openalex.org/W2270470215","https://openalex.org/W2288074780","https://openalex.org/W2518633731","https://openalex.org/W2598634450","https://openalex.org/W2606377603","https://openalex.org/W2759690896","https://openalex.org/W2770106921","https://openalex.org/W2785232201","https://openalex.org/W2894702700","https://openalex.org/W2895347732","https://openalex.org/W2901622658","https://openalex.org/W2907255073","https://openalex.org/W2916879374","https://openalex.org/W2929050222","https://openalex.org/W2962968413","https://openalex.org/W2963026686","https://openalex.org/W2963855167","https://openalex.org/W2963993350","https://openalex.org/W2964121744","https://openalex.org/W2965144482","https://openalex.org/W2968356384","https://openalex.org/W2984911178","https://openalex.org/W2991288262","https://openalex.org/W3001316388","https://openalex.org/W3012698329","https://openalex.org/W3099206234","https://openalex.org/W6631190155","https://openalex.org/W6696429117","https://openalex.org/W6704286305","https://openalex.org/W6735531217","https://openalex.org/W6748218416","https://openalex.org/W6760685730","https://openalex.org/W6772938244","https://openalex.org/W6775189568"],"related_works":["https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W2140186469","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W2775233965","https://openalex.org/W4318559728","https://openalex.org/W1482441085","https://openalex.org/W2966858528","https://openalex.org/W2151687600"],"abstract_inverted_index":{"One":[0],"of":[1,36,84,131,153,159,183],"the":[2,23,34,41,57,78,82,95,132,136,147,151,160,165,190],"most":[3],"significant":[4],"challenges":[5],"in":[6,25,108,172],"Human":[7,58,125],"Activity":[8,59,126],"Recognition":[9,60,127],"using":[10,81],"wearable":[11],"devices":[12],"is":[13,194],"inter-class":[14,202],"similarities":[15,74,203],"and":[16,53,114,178,188,204],"subject":[17,205],"heterogeneity.":[18],"These":[19],"problems":[20],"lead":[21],"to":[22,56,149],"difficulties":[24],"constructing":[26,69],"robust":[27],"feature":[28,196],"representations":[29,197],"that":[30,141,195],"might":[31],"negatively":[32],"affect":[33],"quality":[35,152],"recognition.":[37,154],"This":[38],"study,":[39,174],"for":[40,68,116,168],"first":[42],"time,":[43],"applies":[44],"deep":[45,89,118,142,185],"triplet":[46,50,90,162,186],"networks":[47,99,163,187],"with":[48,100],"various":[49],"loss":[51],"functions":[52],"mining":[54],"methods":[55],"task.":[61],"Moreover,":[62],"we":[63],"introduce":[64],"a":[65],"novel":[66],"method":[67],"hard":[70],"triplets":[71],"by":[72,201],"exploiting":[73],"between":[75],"subjects":[76],"performing":[77],"same":[79],"activities":[80],"concept":[83],"Hierarchical":[85],"Triplet":[86],"Loss.":[87],"Our":[88],"models":[91,121,134,139],"are":[92,198],"based":[93],"on":[94,122],"recent":[96,137],"state-of-the-art":[97,166],"LSTM":[98],"two":[101],"attention":[102],"mechanisms.":[103],"The":[104,129],"extensive":[105],"experiments":[106],"conducted":[107],"this":[109,173],"paper":[110],"identify":[111],"important":[112],"hyperparameters":[113],"settings":[115],"training":[117],"metric":[119,143],"learning":[120,144],"widely-used":[123],"open-source":[124],"datasets.":[128],"comparison":[130],"proposed":[133,191],"against":[135],"benchmark":[138],"shows":[140,164],"approach":[145],"has":[146],"potential":[148],"improve":[150],"Specifically,":[155],"at":[156],"least":[157],"one":[158],"implemented":[161],"results":[167],"each":[169],"dataset":[170],"used":[171],"namely":[175],"PAMAP2,":[176],"USC-HAD":[177],"MHEALTH.":[179],"Another":[180],"positive":[181],"effect":[182],"applying":[184],"especially":[189],"sampling":[192],"algorithm":[193],"less":[199],"affected":[200],"heterogeneity":[206],"issues.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
