{"id":"https://openalex.org/W4405270496","doi":"https://doi.org/10.1109/bsn63547.2024.10780546","title":"In-The-Wild HRV-Based Stress Detection Using Individual-Aware Metric Learning","display_name":"In-The-Wild HRV-Based Stress Detection Using Individual-Aware Metric Learning","publication_year":2024,"publication_date":"2024-10-15","ids":{"openalex":"https://openalex.org/W4405270496","doi":"https://doi.org/10.1109/bsn63547.2024.10780546"},"language":"en","primary_location":{"id":"doi:10.1109/bsn63547.2024.10780546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bsn63547.2024.10780546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Body Sensor Networks (BSN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003337858","display_name":"Jingchun Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jing-Chun Wang","raw_affiliation_strings":["National Tsing Hua University,Department of Electrical Engineering,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Electrical Engineering,Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068933269","display_name":"Woan-Shiuan Chien","orcid":"https://orcid.org/0000-0003-2235-4080"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Woan-Shiuan Chien","raw_affiliation_strings":["National Tsing Hua University,Department of Electrical Engineering,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Electrical Engineering,Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101568708","display_name":"Huanyu Chen","orcid":"https://orcid.org/0000-0002-0055-8034"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Huan-Yu Chen","raw_affiliation_strings":["National Tsing Hua University,Department of Electrical Engineering,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Electrical Engineering,Taiwan","institution_ids":["https://openalex.org/I25846049"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086107623","display_name":"Chi-Chun Lee","orcid":"https://orcid.org/0000-0003-0186-4321"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chi-Chun Lee","raw_affiliation_strings":["National Tsing Hua University,Department of Electrical Engineering,Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Tsing Hua University,Department of Electrical Engineering,Taiwan","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I25846049"],"apc_list":null,"apc_paid":null,"fwci":5.3344,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.96019747,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9593999981880188,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9593999981880188,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.945900022983551,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.646165668964386},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6121905446052551},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.5576184988021851},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4194324314594269},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16097047924995422}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.646165668964386},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6121905446052551},{"id":"https://openalex.org/C21036866","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.5576184988021851},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4194324314594269},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16097047924995422},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bsn63547.2024.10780546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bsn63547.2024.10780546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 20th International Conference on Body Sensor Networks (BSN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1972304037","https://openalex.org/W2025308381","https://openalex.org/W2030696400","https://openalex.org/W2123883893","https://openalex.org/W2146828903","https://openalex.org/W2157832366","https://openalex.org/W2166624057","https://openalex.org/W2964271799","https://openalex.org/W2979623968","https://openalex.org/W3011735246","https://openalex.org/W4281620586","https://openalex.org/W4309973150","https://openalex.org/W4380079526","https://openalex.org/W4392902471"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Advanced":[0],"wearable":[1,26],"tracking":[2],"shows":[3,101],"potential":[4],"for":[5],"identifying":[6],"psychological":[7],"and":[8,36,98,118,122,131],"emotional":[9],"stress":[10,73,106],"relevant":[11],"to":[12,42,61,86],"the":[13,30,78,82,96],"mental":[14],"health":[15],"of":[16,95,110,115,120,125,129,133],"high-intensity":[17],"emergency":[18],"responders.":[19],"Heart":[20],"rate":[21],"variability":[22],"(HRV)":[23],"captured":[24],"by":[25],"devices":[27],"can":[28],"indi-cate":[29],"correlation":[31],"between":[32],"intra-subject":[33,63],"daily":[34],"variations":[35],"stress.":[37],"HRV":[38,59],"also":[39],"varies":[40],"due":[41],"various":[43,88],"demographic":[44],"attributes,":[45,90],"representing":[46],"inter-subject":[47,66],"relationships.":[48],"This":[49],"work":[50],"introduces":[51],"an":[52],"individual-aware":[53],"metric":[54,83],"learning":[55,84],"approach":[56],"that":[57],"leverages":[58],"features":[60],"train":[62],"representations,":[64],"considering":[65],"effects":[67],"based":[68],"on":[69],"attribute":[70],"similarity":[71],"through":[72],"label":[74],"clustering.":[75],"We":[76],"use":[77],"multi-similarity":[79],"loss":[80],"within":[81],"framework":[85],"consider":[87],"personal":[89],"thereby":[91],"improving":[92],"discriminability.":[93],"Evaluation":[94],"TILES-2018":[97],"Firefighter":[99],"database":[100],"promising":[102],"results":[103],"in":[104],"binary":[105],"classification:":[107],"F1":[108,123],"score":[109,124],"68.15":[111],"%":[112,117],"with":[113,127],"BACC":[114,128],"59.13":[116],"MCC":[119,132],"0.186,":[121],"73.07%":[126],"56.52%":[130],"0.136.":[134],"resnectively.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}