{"id":"https://openalex.org/W2604618023","doi":"https://doi.org/10.3233/978-1-61499-722-1-444","title":"HRVBased Stress Recognizing by Random Forest","display_name":"HRVBased Stress Recognizing by Random Forest","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2604618023","doi":"https://doi.org/10.3233/978-1-61499-722-1-444","mag":"2604618023"},"language":"en","primary_location":{"id":"doi:10.3233/978-1-61499-722-1-444","is_oa":false,"landing_page_url":"https://doi.org/10.3233/978-1-61499-722-1-444","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","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/A5102889360","display_name":"Zheng Gang","orcid":"https://orcid.org/0000-0003-1934-3401"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng Gang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116338263","display_name":"Chen Yan-Hui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen Yan-Hui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112121793","display_name":"Min Dai","orcid":"https://orcid.org/0000-0002-1301-7982"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.35173038,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14225","display_name":"Advanced Sensor and Control Systems","score":0.6888999938964844,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14225","display_name":"Advanced Sensor and Control Systems","score":0.6888999938964844,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/random-forest","display_name":"Random forest","score":0.6269453763961792},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.41980522871017456},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.3523828387260437},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3294692635536194},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.32681652903556824},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2872108817100525},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22036278247833252},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.04937434196472168},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.04401475191116333}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6269453763961792},{"id":"https://openalex.org/C21036866","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.41980522871017456},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.3523828387260437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3294692635536194},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.32681652903556824},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2872108817100525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22036278247833252},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.04937434196472168},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.04401475191116333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/978-1-61499-722-1-444","is_oa":false,"landing_page_url":"https://doi.org/10.3233/978-1-61499-722-1-444","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W3135126032","https://openalex.org/W2390279801"],"abstract_inverted_index":{"When":[0],"attempting":[1],"to":[2,15,28,30,55,107,121,138,154],"recognize":[3,56],"mental":[4],"stress":[5,22,38,57,96,125,159],"using":[6,59,83],"heart":[7],"rate":[8],"variability":[9],"(HRV),":[10],"single":[11],"classification":[12],"models":[13],"tend":[14],"have":[16],"lower":[17],"accuracy":[18,36,172],"in":[19,173],"detecting":[20],"different":[21,161],"levels":[23,162,176],"accurately,":[24],"and":[25,51,89,111,143,157,169],"are":[26],"likely":[27],"lead":[29],"over":[31],"fitting,":[32],"therefore":[33],"affecting":[34],"the":[35,43,63,71,101,117,130,148],"of":[37,47,70,114,124,160,167,177],"recognition.":[39],"This":[40],"study":[41],"employed":[42],"ensemble":[44],"learning":[45],"method":[46,54],"random":[48],"forests":[49],"(RF)":[50],"proposed":[52],"a":[53,74,84,95,164],"by":[58],"HRV.":[60],"By":[61],"analyzing":[62],"short-term":[64],"(120&amp;ndash;180":[65],"sec)":[66],"electrocardiography":[67],"(ECG)":[68],"data":[69,127],"subjects":[72],"during":[73],"stress-inductive":[75],"video":[76],"game,":[77],"we":[78,93],"extracted":[79],"their":[80],"HRV":[81],"readings":[82],"time-domain":[85],"method,":[86,88],"frequency-domain":[87],"non-linear":[90],"method.":[91],"Next":[92],"constructed":[94],"recognition":[97],"model":[98,118,150],"based":[99],"on":[100],"RF":[102,149],"technique,":[103],"which":[104],"was":[105,119],"able":[106],"identify":[108,158],"low,":[109],"medium,":[110],"high":[112],"level":[113,126,166],"stress.":[115,178],"Then":[116],"applied":[120],"200":[122],"groups":[123],"collected":[128],"from":[129],"10":[131],"subjects.":[132],"The":[133],"results":[134],"showed":[135],"that,":[136],"compared":[137],"traditional":[139],"k-nearest":[140],"neighbor":[141],"(KNN)":[142],"logistic":[144],"regression":[145],"(LR)":[146],"methods,":[147],"could":[151],"be":[152],"used":[153],"automatically":[155],"detect":[156],"with":[163,170],"higher":[165,175],"accuracy,":[168],"90%":[171],"recognizing":[174]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
