{"id":"https://openalex.org/W4412963037","doi":"https://doi.org/10.1109/i2mtc62753.2025.11079011","title":"Uncertainty Analysis for Deep Learning Prediction in Human Activity Recognition based on Monte Carlo Approach","display_name":"Uncertainty Analysis for Deep Learning Prediction in Human Activity Recognition based on Monte Carlo Approach","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W4412963037","doi":"https://doi.org/10.1109/i2mtc62753.2025.11079011"},"language":"en","primary_location":{"id":"doi:10.1109/i2mtc62753.2025.11079011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc62753.2025.11079011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","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/A5041117762","display_name":"Minh Long Hoang","orcid":"https://orcid.org/0000-0002-3622-4327"},"institutions":[{"id":"https://openalex.org/I124601658","display_name":"University of Parma","ror":"https://ror.org/02k7wn190","country_code":"IT","type":"education","lineage":["https://openalex.org/I124601658"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Minh Long Hoang","raw_affiliation_strings":["University of Parma,Dept. Engineering and Architecture,Parma,Italy"],"affiliations":[{"raw_affiliation_string":"University of Parma,Dept. Engineering and Architecture,Parma,Italy","institution_ids":["https://openalex.org/I124601658"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063818521","display_name":"C. Svelto","orcid":"https://orcid.org/0000-0002-1910-7575"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Cesare Svelto","raw_affiliation_strings":["DEIB Politecnico di Milano,Milano,Italy"],"affiliations":[{"raw_affiliation_string":"DEIB Politecnico di Milano,Milano,Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054454582","display_name":"Paolo Ciampolini","orcid":"https://orcid.org/0000-0001-8944-2152"},"institutions":[{"id":"https://openalex.org/I124601658","display_name":"University of Parma","ror":"https://ror.org/02k7wn190","country_code":"IT","type":"education","lineage":["https://openalex.org/I124601658"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Ciampolini","raw_affiliation_strings":["University of Parma,Dept. Engineering and Architecture,Parma,Italy"],"affiliations":[{"raw_affiliation_string":"University of Parma,Dept. Engineering and Architecture,Parma,Italy","institution_ids":["https://openalex.org/I124601658"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023818682","display_name":"Guido Matrella","orcid":"https://orcid.org/0000-0002-0705-527X"},"institutions":[{"id":"https://openalex.org/I124601658","display_name":"University of Parma","ror":"https://ror.org/02k7wn190","country_code":"IT","type":"education","lineage":["https://openalex.org/I124601658"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Guido Matrella","raw_affiliation_strings":["University of Parma,Dept. Engineering and Architecture,Parma,Italy"],"affiliations":[{"raw_affiliation_string":"University of Parma,Dept. Engineering and Architecture,Parma,Italy","institution_ids":["https://openalex.org/I124601658"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002137798","display_name":"G. Chiorboli","orcid":"https://orcid.org/0000-0001-7399-1827"},"institutions":[{"id":"https://openalex.org/I124601658","display_name":"University of Parma","ror":"https://ror.org/02k7wn190","country_code":"IT","type":"education","lineage":["https://openalex.org/I124601658"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giovanni Chiorboli","raw_affiliation_strings":["University of Parma,Dept. Engineering and Architecture,Parma,Italy"],"affiliations":[{"raw_affiliation_string":"University of Parma,Dept. Engineering and Architecture,Parma,Italy","institution_ids":["https://openalex.org/I124601658"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5041117762"],"corresponding_institution_ids":["https://openalex.org/I124601658"],"apc_list":null,"apc_paid":null,"fwci":1.2826,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.82634608,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.994700014591217,"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":0.994700014591217,"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":0.9904999732971191,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/monte-carlo-method","display_name":"Monte Carlo method","score":0.7234848141670227},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6960747241973877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6113459467887878},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5307885408401489},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5090248584747314},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12098214030265808},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11526098847389221}],"concepts":[{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7234848141670227},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6960747241973877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6113459467887878},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5307885408401489},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5090248584747314},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12098214030265808},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11526098847389221}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/i2mtc62753.2025.11079011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc62753.2025.11079011","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","raw_type":"proceedings-article"},{"id":"pmh:oai:re.public.polimi.it:11311/1310224","is_oa":false,"landing_page_url":"https://hdl.handle.net/11311/1310224","pdf_url":null,"source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W2395419086","https://openalex.org/W2979693376","https://openalex.org/W3036689711","https://openalex.org/W3089186933","https://openalex.org/W3106746018","https://openalex.org/W3108017380","https://openalex.org/W3110590051","https://openalex.org/W3121198188","https://openalex.org/W3193906736","https://openalex.org/W3210766530","https://openalex.org/W4221003122","https://openalex.org/W4285818870","https://openalex.org/W4312609381","https://openalex.org/W4312925166","https://openalex.org/W4313039183","https://openalex.org/W4318464273","https://openalex.org/W4361274042","https://openalex.org/W4379876754","https://openalex.org/W4380996190","https://openalex.org/W4388623142","https://openalex.org/W4390177005","https://openalex.org/W4391128353","https://openalex.org/W4392845099","https://openalex.org/W4403135651","https://openalex.org/W4403302184","https://openalex.org/W4404562926","https://openalex.org/W4405384447","https://openalex.org/W4407358232"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3082895349"],"abstract_inverted_index":{"This":[0,65],"paper":[1],"explores":[2],"uncertainty":[3,152],"estimation":[4],"in":[5,21],"neural":[6],"network":[7],"predictions":[8,60,189],"for":[9,38,61,68,151,190],"human":[10],"activity":[11],"recognition":[12],"using":[13],"a":[14,22,126],"Monte":[15,43,130],"Carlo":[16,44,131],"approach.":[17],"An":[18],"accelerometer":[19],"embedded":[20],"wearable":[23],"device":[24],"on":[25,108,122,154],"the":[26,39,62,92,123,135,139,164,176,179,186,191,201],"user's":[27],"chest":[28],"provides":[29,134],"input":[30,96],"features":[31],"such":[32],"as":[33],"acceleration":[34,107,158],"along":[35,144],"three":[36],"axes":[37],"deep":[40],"learning":[41],"model.":[42],"Dropout":[45,132],"is":[46,197],"employed":[47],"during":[48],"both":[49],"training":[50],"and":[51,71,80,111,141],"inference":[52],"to":[53,105,174],"simulate":[54],"multiple":[55],"forward":[56],"passes,":[57],"enabling":[58],"different":[59],"same":[63],"input.":[64],"method":[66],"allows":[67],"detailed":[69],"analysis":[70],"visualization":[72],"of":[73,94,138,157,163,167,207],"prediction":[74],"uncertainty.":[75],"Among":[76],"all":[77,168],"activities,":[78],"cough":[79,140],"fall":[81,142],"are":[82,87,172],"two":[83],"main":[84],"classes":[85],"which":[86,182],"under":[88],"significant":[89],"concern,":[90],"so":[91],"variation":[93,104],"each":[95,106,155],"feature":[97,177],"will":[98,118],"be":[99,119],"examined":[100],"by":[101],"injecting":[102],"noise":[103],"X,":[109],"Y":[110],"Z":[112],"axis":[113],"subsequently.":[114],"New":[115],"noisy":[116],"data":[117,124],"generated":[120,170],"based":[121,153],"from":[125],"real":[127],"dataset,":[128],"then":[129],"model":[133],"predicted":[136],"probability":[137],"class":[143],"with":[145,178],"their":[146],"associated":[147],"95%":[148],"confidence":[149,165],"interval":[150],"type":[156],"fluctuation.":[159],"The":[160,194],"average":[161],"width":[162],"intervals":[166],"new":[169],"samples":[171],"calculated":[173],"identify":[175],"lowest":[180],"uncertainty,":[181],"would":[183],"generally":[184],"deliver":[185],"most":[187],"reliable":[188],"given":[192],"class.":[193],"utilized":[195],"approach":[196],"highly":[198],"essential":[199],"when":[200],"obtained":[202],"dataset":[203],"has":[204],"limited":[205],"amount":[206],"data.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-28T08:17:26.163206","created_date":"2025-10-26T00:00:00"}