{"id":"https://openalex.org/W2551239383","doi":"https://doi.org/10.1109/ijcnn.2016.7727224","title":"Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors","display_name":"Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2551239383","doi":"https://doi.org/10.1109/ijcnn.2016.7727224","mag":"2551239383"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5078646842","display_name":"Sojeong Ha","orcid":null},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Sojeong Ha","raw_affiliation_strings":["Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","institution_ids":["https://openalex.org/I123900574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101795797","display_name":"Seungjin Choi","orcid":"https://orcid.org/0000-0002-8301-3776"},"institutions":[{"id":"https://openalex.org/I123900574","display_name":"Pohang University of Science and Technology","ror":"https://ror.org/04xysgw12","country_code":"KR","type":"education","lineage":["https://openalex.org/I123900574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungjin Choi","raw_affiliation_strings":["Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea","institution_ids":["https://openalex.org/I123900574"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078646842"],"corresponding_institution_ids":["https://openalex.org/I123900574"],"apc_list":null,"apc_paid":null,"fwci":11.657,"has_fulltext":false,"cited_by_count":305,"citation_normalized_percentile":{"value":0.99007081,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"381","last_page":"388"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9969000220298767,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8082276582717896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7411037087440491},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6561538577079773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6347168684005737},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.5962400436401367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.58432537317276},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5728989839553833},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.5522312521934509},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5202394723892212},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5049131512641907},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.44529256224632263},{"id":"https://openalex.org/keywords/gyroscope","display_name":"Gyroscope","score":0.43442803621292114},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.42700278759002686},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34664174914360046},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.30397045612335205},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.26989316940307617},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11450052261352539}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8082276582717896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7411037087440491},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6561538577079773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6347168684005737},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.5962400436401367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.58432537317276},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5728989839553833},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.5522312521934509},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5202394723892212},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5049131512641907},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.44529256224632263},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.43442803621292114},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.42700278759002686},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34664174914360046},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.30397045612335205},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26989316940307617},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11450052261352539},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W123295786","https://openalex.org/W1515966130","https://openalex.org/W1585610923","https://openalex.org/W1606768424","https://openalex.org/W1665214252","https://openalex.org/W1910567995","https://openalex.org/W1997086460","https://openalex.org/W2002261403","https://openalex.org/W2062227835","https://openalex.org/W2101603727","https://openalex.org/W2103151433","https://openalex.org/W2105046342","https://openalex.org/W2112796928","https://openalex.org/W2118585731","https://openalex.org/W2131147042","https://openalex.org/W2145835757","https://openalex.org/W2155273149","https://openalex.org/W2162315509","https://openalex.org/W2164055860","https://openalex.org/W2243012843","https://openalex.org/W2288074780","https://openalex.org/W2530593441","https://openalex.org/W6637242042","https://openalex.org/W6677656871","https://openalex.org/W6681579763","https://openalex.org/W6683885849","https://openalex.org/W6696429117","https://openalex.org/W6728390481"],"related_works":["https://openalex.org/W2356006086","https://openalex.org/W2545168295","https://openalex.org/W2365897603","https://openalex.org/W4234814094","https://openalex.org/W2156308897","https://openalex.org/W4303613760","https://openalex.org/W2361871310","https://openalex.org/W2417246878","https://openalex.org/W1982154684","https://openalex.org/W2404620998"],"abstract_inverted_index":{"Human":[0],"activity":[1,36],"recognition":[2],"involves":[3],"classifying":[4],"times":[5],"series":[6],"data,":[7,100],"measured":[8],"at":[9],"inertial":[10],"sensors":[11],"such":[12,169],"as":[13,30,175,177],"accelerometers":[14],"or":[15,69],"gyroscopes,":[16],"into":[17],"one":[18],"of":[19,49,54,110,204,211],"pre-defined":[20],"actions.":[21],"Recently,":[22],"convolutional":[23],"neural":[24],"network":[25],"(CNN)":[26],"has":[27],"established":[28],"itself":[29],"a":[31,170],"powerful":[32],"technique":[33],"for":[34,98,117,151,164],"human":[35],"recognition,":[37],"where":[38],"convolution":[39,58,76],"and":[40,77,95,147,160,189],"pooling":[41,78],"operations":[42,79],"are":[43,80,182,190],"applied":[44,61,81],"along":[45,92],"the":[46,129,201,212],"temporal":[47,94],"dimension":[48],"sensor":[50],"signals.":[51],"In":[52,140],"most":[53],"existing":[55,120],"work,":[56],"1D":[57,114],"operation":[59,124],"is":[60],"to":[62,82,88,113,209],"individual":[63],"univariate":[64],"time":[65,73,84],"series,":[66,85],"while":[67],"multi-sensors":[68],"multi-modality":[70],"yield":[71],"multivariate":[72,83],"series.":[74],"2D":[75,123],"in":[86,128,168,193],"order":[87],"capture":[89],"local":[90],"dependency":[91],"both":[93,156],"spatial":[96],"domains":[97],"uni-modal":[99],"so":[101],"that":[102,172],"it":[103],"achieves":[104],"high":[105,202],"performance":[106,203],"with":[107,122],"less":[108],"number":[109],"parameters":[111],"compared":[112,208],"operation.":[115],"However":[116],"multi-modal":[118,152,185],"data":[119,188],"CNNs":[121,145],"handle":[125],"different":[126,138],"modalities":[127,181],"same":[130],"way,":[131],"which":[132],"cause":[133],"interferences":[134],"between":[135],"characteristics":[136,174,179],"from":[137,184],"modalities.":[139],"this":[141],"paper,":[142],"we":[143],"present":[144],"(CNN-pf":[146],"CNN-pff),":[148],"especially":[149],"CNN-pff,":[150],"data.":[153],"We":[154],"employ":[155],"partial":[157],"weight":[158,162],"sharing":[159,163],"full":[161],"our":[165,205],"CNN":[166,206],"models":[167],"way":[171],"modality-specific":[173],"well":[176],"common":[178],"across":[180],"learned":[183],"(or":[186],"multi-sensor)":[187],"eventually":[191],"aggregated":[192],"upper":[194],"layers.":[195],"Experiments":[196],"on":[197],"benchmark":[198],"datasets":[199],"demonstrate":[200],"models,":[207],"state":[210],"arts":[213],"methods.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":34},{"year":2023,"cited_by_count":40},{"year":2022,"cited_by_count":39},{"year":2021,"cited_by_count":57},{"year":2020,"cited_by_count":42},{"year":2019,"cited_by_count":36},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":9}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}