{"id":"https://openalex.org/W3017361427","doi":"https://doi.org/10.1109/icaiic48513.2020.9065078","title":"A CNN-LSTM Approach to Human Activity Recognition","display_name":"A CNN-LSTM Approach to Human Activity Recognition","publication_year":2020,"publication_date":"2020-02-01","ids":{"openalex":"https://openalex.org/W3017361427","doi":"https://doi.org/10.1109/icaiic48513.2020.9065078","mag":"3017361427"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic48513.2020.9065078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic48513.2020.9065078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","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/A5027819256","display_name":"Ronald Mutegeki","orcid":"https://orcid.org/0000-0001-8933-1487"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Ronald Mutegeki","raw_affiliation_strings":["School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086165395","display_name":"Dong Seog Han","orcid":"https://orcid.org/0000-0002-7769-0236"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dong Seog Han","raw_affiliation_strings":["School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea","institution_ids":["https://openalex.org/I31419693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027819256"],"corresponding_institution_ids":["https://openalex.org/I31419693"],"apc_list":null,"apc_paid":null,"fwci":22.3782,"has_fulltext":false,"cited_by_count":412,"citation_normalized_percentile":{"value":0.99659367,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"362","last_page":"366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9857000112533569,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9815999865531921,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8572383522987366},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.7927953004837036},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7910237312316895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7705575227737427},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.766594409942627},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6542102694511414},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5859076976776123},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5468586683273315},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4799368977546692},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4634338319301605},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45359936356544495},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43406254053115845},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4199506342411041},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40789759159088135}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8572383522987366},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.7927953004837036},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7910237312316895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7705575227737427},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.766594409942627},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6542102694511414},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5859076976776123},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5468586683273315},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4799368977546692},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4634338319301605},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45359936356544495},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43406254053115845},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4199506342411041},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40789759159088135},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","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/icaiic48513.2020.9065078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic48513.2020.9065078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W134960717","https://openalex.org/W2064675550","https://openalex.org/W2163605009","https://openalex.org/W2344284192","https://openalex.org/W2774090517","https://openalex.org/W2803675191","https://openalex.org/W2889505988","https://openalex.org/W2898843852","https://openalex.org/W2946000228","https://openalex.org/W2963855167","https://openalex.org/W2998219284","https://openalex.org/W6605479355","https://openalex.org/W6684191040","https://openalex.org/W6751648517","https://openalex.org/W6754477931","https://openalex.org/W6755970841","https://openalex.org/W6763335694"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W4391094981","https://openalex.org/W2481123202","https://openalex.org/W2117913171","https://openalex.org/W2582769230","https://openalex.org/W4387022695","https://openalex.org/W4376608661","https://openalex.org/W2759690896"],"abstract_inverted_index":{"To":[0],"understand":[1],"human":[2,7,11,81],"behavior":[3],"and":[4,19,108,125,161],"intrinsically":[5],"anticipate":[6],"intentions,":[8],"research":[9],"into":[10],"activity":[12,40,60],"recognition":[13,61],"HAR)":[14],"using":[15],"sensors":[16],"in":[17,158],"wearable":[18],"handheld":[20],"devices":[21],"has":[22],"intensified.":[23],"The":[24,102],"ability":[25],"for":[26,98],"a":[27,38,56,63,115,126],"system":[28],"to":[29,36],"use":[30],"as":[31,34],"few":[32],"resources":[33],"possible":[35],"recognize":[37],"user's":[39],"from":[41,83],"raw":[42,84],"data":[43,85],"is":[44,105],"what":[45],"many":[46],"researchers":[47],"are":[48],"striving":[49],"for.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54],"propose":[55],"holistic":[57],"deep":[58,149],"learning-based":[59],"architecture,":[62],"convolutional":[64],"neural":[65,150],"network-long":[66],"short-term":[67],"memory":[68],"network":[69,104,151],"(CNN-LSTM).":[70],"This":[71],"CNN-LSTM":[72,103],"approach":[73],"not":[74],"only":[75],"improves":[76],"the":[77,89,92,96,119,131,159],"predictive":[78],"accuracy":[79,117,129],"of":[80,91],"activities":[82],"but":[86],"also":[87,137],"reduces":[88],"complexity":[90],"model":[93,113],"while":[94],"eliminating":[95],"need":[97],"advanced":[99],"feature":[100,171],"engineering.":[101],"both":[106],"spatially":[107],"temporally":[109],"deep.":[110],"Our":[111],"proposed":[112,157],"achieves":[114],"99%":[116],"on":[118,130,168],"iSPL":[120],"dataset,":[121,124],"an":[122],"internal":[123],"92":[127],"%":[128],"UCI":[132],"HAR":[133],"public":[134],"dataset.":[135],"We":[136],"compared":[138],"its":[139],"performance":[140],"against":[141,147,162],"other":[142,148],"approaches.":[143],"It":[144],"competes":[145],"favorably":[146],"(DNN)":[152],"architectures":[153],"that":[154,166],"have":[155],"been":[156],"past":[160],"machine":[163],"learning":[164],"models":[165],"rely":[167],"manually":[169],"engineered":[170],"datasets.":[172]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":73},{"year":2024,"cited_by_count":101},{"year":2023,"cited_by_count":104},{"year":2022,"cited_by_count":74},{"year":2021,"cited_by_count":44},{"year":2020,"cited_by_count":6}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}
