{"id":"https://openalex.org/W3044286661","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207697","title":"Classifying Imbalanced Multi-modal Sensor Data for Human Activity Recognition in a Smart Home using Deep Learning","display_name":"Classifying Imbalanced Multi-modal Sensor Data for Human Activity Recognition in a Smart Home using Deep Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3044286661","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207697","mag":"3044286661"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/Classifying_imbalanced_multi-modal_sensor_data_for_human_activity_recognition_in_a_smart_home_using_deep_learning/13061723","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002798139","display_name":"Ali A. Alani","orcid":"https://orcid.org/0000-0003-4765-5779"},"institutions":[{"id":"https://openalex.org/I78277041","display_name":"University of Diyala","ror":"https://ror.org/01eb5yv70","country_code":"IQ","type":"education","lineage":["https://openalex.org/I78277041"]}],"countries":["IQ"],"is_corresponding":true,"raw_author_name":"Ali A. Alani","raw_affiliation_strings":["Department of Computer Science, University of Diyala, Diyala, Iraq"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Diyala, Diyala, Iraq","institution_ids":["https://openalex.org/I78277041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022173527","display_name":"Georgina Cosma","orcid":"https://orcid.org/0000-0002-4663-6907"},"institutions":[{"id":"https://openalex.org/I143804889","display_name":"Loughborough University","ror":"https://ror.org/04vg4w365","country_code":"GB","type":"education","lineage":["https://openalex.org/I143804889"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Georgina Cosma","raw_affiliation_strings":["Department of Computer Science, Loughborough University, Loughborough, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Loughborough University, Loughborough, UK","institution_ids":["https://openalex.org/I143804889"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029315420","display_name":"Aboozar Taherkhani","orcid":"https://orcid.org/0000-0002-3627-6362"},"institutions":[{"id":"https://openalex.org/I66943878","display_name":"De Montfort University","ror":"https://ror.org/0312pnr83","country_code":"GB","type":"education","lineage":["https://openalex.org/I66943878"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Aboozar Taherkhani","raw_affiliation_strings":["School of Science and Informatics, De Montfort University, Leicester, UK"],"affiliations":[{"raw_affiliation_string":"School of Science and Informatics, De Montfort University, Leicester, UK","institution_ids":["https://openalex.org/I66943878"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002798139"],"corresponding_institution_ids":["https://openalex.org/I78277041"],"apc_list":null,"apc_paid":null,"fwci":1.7664,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.87290876,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9957000017166138,"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":0.9957000017166138,"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":0.9902999997138977,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7684166431427002},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.74262934923172},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7118608355522156},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6877055764198303},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6234549880027771},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6003350019454956},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5722402930259705},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5674633383750916},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.547256588935852},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34787195920944214}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7684166431427002},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.74262934923172},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7118608355522156},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6877055764198303},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6234549880027771},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6003350019454956},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5722402930259705},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5674633383750916},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.547256588935852},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34787195920944214},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207697","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:figshare.com:article/13061723","is_oa":true,"landing_page_url":null,"pdf_url":"https://figshare.com/articles/conference_contribution/Classifying_imbalanced_multi-modal_sensor_data_for_human_activity_recognition_in_a_smart_home_using_deep_learning/13061723","source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"pmh:oai:dora.dmu.ac.uk:2086/19912","is_oa":true,"landing_page_url":"https://dora.dmu.ac.uk/handle/2086/19912","pdf_url":null,"source":{"id":"https://openalex.org/S4306400394","display_name":"DMU Open Research Archive (De Montfort University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66943878","host_organization_name":"De Montfort University","host_organization_lineage":["https://openalex.org/I66943878"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/13061723","is_oa":true,"landing_page_url":null,"pdf_url":"https://figshare.com/articles/conference_contribution/Classifying_imbalanced_multi-modal_sensor_data_for_human_activity_recognition_in_a_smart_home_using_deep_learning/13061723","source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3044286661.pdf","grobid_xml":"https://content.openalex.org/works/W3044286661.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1600744878","https://openalex.org/W2011376672","https://openalex.org/W2148143831","https://openalex.org/W2290857936","https://openalex.org/W2339172597","https://openalex.org/W2563686712","https://openalex.org/W2580840020","https://openalex.org/W2584755547","https://openalex.org/W2594590268","https://openalex.org/W2608928662","https://openalex.org/W2760948057","https://openalex.org/W2767106145","https://openalex.org/W2889445182","https://openalex.org/W2899095735","https://openalex.org/W2945490067","https://openalex.org/W3006635138","https://openalex.org/W6696811367","https://openalex.org/W6734772259","https://openalex.org/W6755524025"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W2781247653","https://openalex.org/W4206637278","https://openalex.org/W4386005305","https://openalex.org/W4386214543","https://openalex.org/W3082051559","https://openalex.org/W3022501507","https://openalex.org/W4205824301","https://openalex.org/W3188632291","https://openalex.org/W2023639956"],"abstract_inverted_index":{"In":[0],"smart":[1],"homes,":[2],"data":[3,47,81,102,135,165,196],"generated":[4,122],"from":[5,123],"real-time":[6],"sensors":[7],"for":[8,127,180],"human":[9],"activity":[10],"recognition":[11],"is":[12,18],"complex,":[13],"noisy":[14],"and":[15,91,108,145,166,183,197,221],"imbalanced.":[16],"It":[17],"a":[19,117,130,172],"significant":[20],"challenge":[21],"to":[22,44,87,96,147,205],"create":[23],"machine":[24],"learning":[25,41,51,58,94,110,186],"models":[26,42,64],"that":[27,192],"can":[28],"classify":[29,45],"activities":[30,151],"which":[31,68],"are":[32,48],"not":[33],"as":[34,37,85],"commonly":[35,54],"occurring":[36,55],"other":[38],"activities.":[39],"Machine":[40],"designed":[43],"imbalanced":[46,78,99,163],"biased":[49],"towards":[50],"the":[52,63,124,158,168,189,195,199,211],"more":[53,70],"classes.":[56],"Such":[57],"bias":[59],"occurs":[60],"naturally,":[61],"since":[62],"better":[65],"learn":[66],"classes":[67],"contain":[69],"records.":[71],"This":[72],"paper":[73],"examines":[74],"whether":[75],"fusing":[76,194],"real-world":[77],"multi-modal":[79,100,119,164],"sensor":[80,101,120],"improves":[82],"classification":[83,213],"results":[84,154,190],"opposed":[86],"using":[88,104,116,155,198],"unimodal":[89],"data;":[90],"compares":[92],"deep":[93,109,185],"approaches":[95],"dealing":[97,161],"with":[98,162],"when":[103,193],"various":[105],"resampling":[106],"methods":[107],"models.":[111,187],"Experiments":[112],"were":[113],"carried":[114],"out":[115],"large":[118],"dataset":[121],"Sensor":[125],"Platform":[126],"HEalthcare":[128],"in":[129],"Residential":[131],"Environment":[132],"(SPHERE).":[133],"The":[134],"comprises":[136,142],"16104":[137],"samples,":[138],"where":[139],"each":[140,178],"sample":[141],"5608":[143],"features":[144],"belongs":[146],"one":[148],"of":[149,160,170,175,215],"20":[150],"(classes).":[152],"Experimental":[153],"SPHERE":[156],"demonstrate":[157],"challenges":[159],"highlight":[167],"importance":[169],"having":[171],"suitable":[173],"number":[174],"samples":[176],"within":[177],"class":[179,207],"sufficiently":[181],"training":[182],"testing":[184],"Furthermore,":[188],"revealed":[191],"Synthetic":[200],"Minority":[201],"Oversampling":[202],"Technique":[203],"(SMOTE)":[204],"correct":[206],"imbalance,":[208],"CNN-LSTM":[209],"achieved":[210],"highest":[212],"accuracy":[214],"93.67%":[216],"followed":[217],"by":[218],"CNN,":[219],"93.55%,":[220],"LSTM,":[222],"i.e.":[223],"92.98%.":[224]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
