{"id":"https://openalex.org/W4206912758","doi":"https://doi.org/10.1109/gcwkshps52748.2021.9681996","title":"IoT-enabled model for Digital Twin Of Mental Stress (DTMS)","display_name":"IoT-enabled model for Digital Twin Of Mental Stress (DTMS)","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4206912758","doi":"https://doi.org/10.1109/gcwkshps52748.2021.9681996"},"language":"en","primary_location":{"id":"doi:10.1109/gcwkshps52748.2021.9681996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps52748.2021.9681996","pdf_url":null,"source":{"id":"https://openalex.org/S4363605397","display_name":"2021 IEEE Globecom Workshops (GC Wkshps)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Globecom Workshops (GC Wkshps)","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/A5019822805","display_name":"Rahatara Ferdousi","orcid":"https://orcid.org/0000-0003-1143-2370"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Rahatara Ferdousi","raw_affiliation_strings":["School of Electrical Engineering and Computer Science (EECS), Multimedia Communications Research Laboratory (MCRLab) University of Ottawa"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science (EECS), Multimedia Communications Research Laboratory (MCRLab) University of Ottawa","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089831439","display_name":"M. Anwar Hossain","orcid":"https://orcid.org/0000-0001-9777-0332"},"institutions":[{"id":"https://openalex.org/I28022161","display_name":"King Saud University","ror":"https://ror.org/02f81g417","country_code":"SA","type":"education","lineage":["https://openalex.org/I28022161"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"M. Anwar Hossain","raw_affiliation_strings":["College of Computer & Information Sciences, King Saud University"],"affiliations":[{"raw_affiliation_string":"College of Computer & Information Sciences, King Saud University","institution_ids":["https://openalex.org/I28022161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109797436","display_name":"Abdulmotaleb El Saddik","orcid":null},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Abdulmotaleb El Saddik","raw_affiliation_strings":["School of Electrical Engineering and Computer Science (EECS), Multimedia Communications Research Laboratory (MCRLab) University of Ottawa"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science (EECS), Multimedia Communications Research Laboratory (MCRLab) University of Ottawa","institution_ids":["https://openalex.org/I153718931"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019822805"],"corresponding_institution_ids":["https://openalex.org/I153718931"],"apc_list":null,"apc_paid":null,"fwci":6.2772,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.9609375,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10168","display_name":"COVID-19 and Mental Health","score":0.9772999882698059,"subfield":{"id":"https://openalex.org/subfields/3203","display_name":"Clinical Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10168","display_name":"COVID-19 and Mental Health","score":0.9772999882698059,"subfield":{"id":"https://openalex.org/subfields/3203","display_name":"Clinical Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.9567999839782715,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9384999871253967,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7455505132675171},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6934933662414551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6335249543190002},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5806854963302612},{"id":"https://openalex.org/keywords/clickstream","display_name":"Clickstream","score":0.5664347410202026},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5545578598976135},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4753703474998474},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4541773200035095},{"id":"https://openalex.org/keywords/stress","display_name":"Stress (linguistics)","score":0.42682385444641113},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4240325391292572},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4239053726196289},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4185045063495636},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.41601958870887756},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.16784018278121948},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.12065061926841736},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.11845725774765015},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.10058918595314026}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7455505132675171},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6934933662414551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6335249543190002},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5806854963302612},{"id":"https://openalex.org/C138744977","wikidata":"https://www.wikidata.org/wiki/Q5132438","display_name":"Clickstream","level":5,"score":0.5664347410202026},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5545578598976135},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4753703474998474},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4541773200035095},{"id":"https://openalex.org/C21036866","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.42682385444641113},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4240325391292572},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4239053726196289},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4185045063495636},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.41601958870887756},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.16784018278121948},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.12065061926841736},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.11845725774765015},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10058918595314026},{"id":"https://openalex.org/C130436687","wikidata":"https://www.wikidata.org/wiki/Q7978591","display_name":"Web modeling","level":3,"score":0.0},{"id":"https://openalex.org/C35578498","wikidata":"https://www.wikidata.org/wiki/Q193424","display_name":"Web service","level":2,"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/C127613066","wikidata":"https://www.wikidata.org/wiki/Q557770","display_name":"Web API","level":4,"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":1,"locations":[{"id":"doi:10.1109/gcwkshps52748.2021.9681996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcwkshps52748.2021.9681996","pdf_url":null,"source":{"id":"https://openalex.org/S4363605397","display_name":"2021 IEEE Globecom Workshops (GC Wkshps)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Globecom Workshops (GC Wkshps)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W321486980","https://openalex.org/W2218733455","https://openalex.org/W2769507557","https://openalex.org/W2788807534","https://openalex.org/W2886253469","https://openalex.org/W2890929258","https://openalex.org/W2895787052","https://openalex.org/W2918153055","https://openalex.org/W2964326881","https://openalex.org/W2965754193","https://openalex.org/W2971117641","https://openalex.org/W3004825562","https://openalex.org/W3006907331","https://openalex.org/W3015197879","https://openalex.org/W3041006163","https://openalex.org/W3165033857","https://openalex.org/W4235421565","https://openalex.org/W6640174009"],"related_works":["https://openalex.org/W2803398878","https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4385770464","https://openalex.org/W3125536479","https://openalex.org/W4214820172","https://openalex.org/W3120363735","https://openalex.org/W2394323384"],"abstract_inverted_index":{"Stress":[0,95],"has":[1],"become":[2],"one":[3],"of":[4,9,17,93],"the":[5,10,27,139],"mental":[6,107],"health":[7],"adversaries":[8],"COVID-19":[11],"pandemic.":[12],"Several":[13],"stressors":[14,41],"like":[15],"fear":[16],"infection,":[18],"lockdown,":[19],"and":[20,61,103,127],"social":[21],"distancing":[22],"are":[23,34],"commonly":[24],"accountable":[25],"for":[26,81,106,114],"stress.":[28],"The":[29,44,65,131],"existing":[30],"stress":[31,108],"prediction":[32,63],"systems":[33],"less":[35],"compatible":[36],"to":[37,74],"handle":[38],"diversly":[39],"changing":[40],"during":[42],"COVID-19.":[43],"traditional":[45],"approaches":[46],"often":[47],"use":[48],"incomplete":[49],"features":[50],"from":[51,77],"limited":[52],"sources":[53,79],"(e.g.,":[54],"only":[55],"wearable":[56],"sensor":[57],"or":[58],"user":[59],"device)":[60],"static":[62],"techniques.":[64],"Edge":[66],"Artificial":[67],"Intelligence":[68],"(Edge":[69],"AI)":[70],"employs":[71,99],"machine":[72,104],"learning":[73,105],"make":[75],"data":[76],"these":[78],"usable":[80],"decision":[82],"making.":[83],"Therefore,":[84],"In":[85],"this":[86],"study,":[87],"we":[88],"propose":[89],"a":[90],"Digital":[91,133],"Twin":[92,134],"Mental":[94],"(DTMS)":[96],"model":[97],"that":[98],"IoT-based":[100],"multimodal":[101],"sensing":[102],"prediction.":[109],"We":[110],"obtained":[111],"98%":[112],"accuracy":[113],"four":[115],"widely":[116],"used":[117],"Machine":[118],"Learning(ML)":[119],"algorithms":[120],"Na\u00efve":[121],"Bayes(NB),":[122],"Random":[123],"Forest(RF),":[124],"Multilayer":[125],"Perceptron(MLP),":[126],"Decision":[128],"Tree":[129],"(DT).":[130],"optimal":[132],"Features":[135],"(DTF)":[136],"could":[137],"reduce":[138],"classification":[140],"time.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}