{"id":"https://openalex.org/W3036630077","doi":"https://doi.org/10.1109/icccs49078.2020.9118506","title":"A study on IMU-Based Human Activity Recognition Using Deep Learning and Traditional Machine Learning","display_name":"A study on IMU-Based Human Activity Recognition Using Deep Learning and Traditional Machine Learning","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3036630077","doi":"https://doi.org/10.1109/icccs49078.2020.9118506","mag":"3036630077"},"language":"en","primary_location":{"id":"doi:10.1109/icccs49078.2020.9118506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs49078.2020.9118506","pdf_url":null,"source":{"id":"https://openalex.org/S4306498617","display_name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","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":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","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/A5112542638","display_name":"Chengli Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengli Hou","raw_affiliation_strings":["Information Engineering School of NanChang University, Nanchang, China"],"affiliations":[{"raw_affiliation_string":"Information Engineering School of NanChang University, Nanchang, China","institution_ids":["https://openalex.org/I141649914"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5112542638"],"corresponding_institution_ids":["https://openalex.org/I141649914"],"apc_list":null,"apc_paid":null,"fwci":1.9625,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.89983526,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"225","last_page":"234"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9947999715805054,"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":0.9925000071525574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8271832466125488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7921484708786011},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7629160284996033},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7248232960700989},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6469815969467163},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6455841660499573},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6287275552749634},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5238811373710632},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4646773934364319}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8271832466125488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7921484708786011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7629160284996033},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7248232960700989},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6469815969467163},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6455841660499573},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6287275552749634},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5238811373710632},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4646773934364319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccs49078.2020.9118506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs49078.2020.9118506","pdf_url":null,"source":{"id":"https://openalex.org/S4306498617","display_name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","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":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","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":26,"referenced_works":["https://openalex.org/W1551782597","https://openalex.org/W1899504021","https://openalex.org/W1915297548","https://openalex.org/W1947481528","https://openalex.org/W2002353621","https://openalex.org/W2017634428","https://openalex.org/W2026297770","https://openalex.org/W2034328688","https://openalex.org/W2073401630","https://openalex.org/W2126511896","https://openalex.org/W2145343602","https://openalex.org/W2148857358","https://openalex.org/W2149216032","https://openalex.org/W2156909104","https://openalex.org/W2247209766","https://openalex.org/W2270470215","https://openalex.org/W2344284192","https://openalex.org/W2551239383","https://openalex.org/W2604630936","https://openalex.org/W2759690896","https://openalex.org/W2789436454","https://openalex.org/W2809191403","https://openalex.org/W2963855167","https://openalex.org/W4249279051","https://openalex.org/W6631636882","https://openalex.org/W6682035786"],"related_works":["https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2811014843","https://openalex.org/W2004826645","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W4372048956","https://openalex.org/W3135818052","https://openalex.org/W3193565141"],"abstract_inverted_index":{"Human":[0],"Activity":[1],"Recognition":[2],"(HAR)":[3],"has":[4,26],"been":[5],"an":[6],"increasingly":[7],"popular":[8],"range":[9],"to":[10,104,169],"do":[11],"researches":[12],"which":[13,57],"stems":[14],"from":[15],"the":[16,43,59,130,140,157,170,173,180,185,190,196,205],"ubiquitous":[17],"computing.":[18],"And":[19],"lately,":[20],"identifying":[21],"activities":[22,47],"during":[23],"daily":[24],"life":[25],"become":[27],"one":[28],"of":[29,45,61,93,122,132,150,172,184,193,210],"more":[30,32,35,37,106,200,221],"and":[31,36,66,78,95,117,154,164,218],"challenges.":[33],"Subsequently,":[34],"methods":[38,72,136],"can":[39],"be":[40],"used":[41],"in":[42,162,166],"recognition":[44],"human":[46],"such":[48,108,215],"as":[49,137,139,216],"Support":[50],"Vector":[51],"Machine":[52,63],"(SVM),":[53],"Random":[54],"Forests":[55],"(RF)":[56],"are":[58,199,220],"representatives":[60],"Traditional":[62],"Learning":[64,70],"(TML)":[65],"also":[67],"some":[68],"Deep":[69],"(DL)":[71],"like":[73,103],"Convolutional":[74],"Neural":[75,80],"Network":[76,81],"(CNN)":[77],"Recurrent":[79],"(RNN).":[82],"However,":[83,202],"neither":[84],"TML":[85,197],"nor":[86],"DL":[87,163,213],"is":[88],"suitable":[89],"for":[90],"all":[91],"kinds":[92,149],"situations":[94],"various":[96],"datasets.":[97,211],"As":[98],"a":[99,115,177],"result,":[100],"we":[101,113,175],"would":[102],"explore":[105],"about":[107],"consequences.":[109],"In":[110],"this":[111],"paper,":[112],"discover":[114],"discrepancy":[116],"phenomenon":[118],"that":[119],"different":[120,151],"sizes":[121],"collected":[123],"HAR":[124,186,191],"datasets":[125,152,192,206],"may":[126],"produce":[127],"influences":[128],"on":[129,147,179],"effectiveness":[131],"traditional":[133],"machine":[134],"learning":[135,142],"well":[138],"deep":[141],"architectures.":[143],"We":[144],"conduct":[145],"experiments":[146,174],"two":[148],"USC-HAD":[153],"WISDM":[155],"with":[156,189],"best":[158],"accuracy":[159],"nearly":[160],"90%":[161],"87%":[165],"TML.":[167],"Due":[168],"consequences":[171],"give":[176],"conclusion":[178],"individual":[181],"heterogeneity":[182],"problems":[183],"datasets-when":[187],"dealing":[188],"small":[194],"scales,":[195],"structures":[198],"suitable.":[201],"conversely,":[203],"when":[204],"have":[207],"large":[208],"amount":[209],"Specifically,":[212],"approaches":[214],"CNN":[217],"LSTM":[219],"sensible":[222],"choices.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}