{"id":"https://openalex.org/W3203829457","doi":"https://doi.org/10.1145/3460418.3479386","title":"Classical machine learning and deep neural network ensemble model for GPS-based activity recognition","display_name":"Classical machine learning and deep neural network ensemble model for GPS-based activity recognition","publication_year":2021,"publication_date":"2021-09-21","ids":{"openalex":"https://openalex.org/W3203829457","doi":"https://doi.org/10.1145/3460418.3479386","mag":"3203829457"},"language":"en","primary_location":{"id":"doi:10.1145/3460418.3479386","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460418.3479386","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers","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/A5058060139","display_name":"Ryoichi Kojima","orcid":"https://orcid.org/0009-0009-3128-7781"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryoichi Kojima","raw_affiliation_strings":["KDDI Research, INC., Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, INC., Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014746910","display_name":"Roberto Legaspi","orcid":"https://orcid.org/0000-0001-8909-635X"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Roberto Legaspi","raw_affiliation_strings":["KDDI Research, INC., Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, INC., Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055163474","display_name":"Yutaro Mishima","orcid":"https://orcid.org/0009-0003-7374-8474"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yutaro Mishima","raw_affiliation_strings":["KDDI Research, INC., Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, INC., Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062786880","display_name":"Shinya Wada","orcid":"https://orcid.org/0000-0001-6009-6655"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shinya Wada","raw_affiliation_strings":["KDDI Research, Inc., Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Japan","institution_ids":["https://openalex.org/I4210164495"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.097,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.40728375,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"369","last_page":"373"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.9761000275611877,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9739999771118164,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7290449142456055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6731516122817993},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6593800783157349},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.624012291431427},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.543714702129364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5197305679321289},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4323383867740631},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.42019200325012207},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4201434552669525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290449142456055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6731516122817993},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6593800783157349},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.624012291431427},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.543714702129364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5197305679321289},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4323383867740631},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.42019200325012207},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4201434552669525},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460418.3479386","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460418.3479386","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers","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":23,"referenced_works":["https://openalex.org/W2019625072","https://openalex.org/W2038592259","https://openalex.org/W2064675550","https://openalex.org/W2180635266","https://openalex.org/W2619383789","https://openalex.org/W2736191430","https://openalex.org/W2736707111","https://openalex.org/W2768348081","https://openalex.org/W2883766876","https://openalex.org/W2899061465","https://openalex.org/W2899151654","https://openalex.org/W2907123474","https://openalex.org/W2911964244","https://openalex.org/W2936601575","https://openalex.org/W2949676527","https://openalex.org/W2972317743","https://openalex.org/W2972378419","https://openalex.org/W2972497135","https://openalex.org/W2973204573","https://openalex.org/W3014683905","https://openalex.org/W3084913306","https://openalex.org/W3099626665","https://openalex.org/W3203725677"],"related_works":["https://openalex.org/W3008584592","https://openalex.org/W3124943098","https://openalex.org/W4308112567","https://openalex.org/W3162132941","https://openalex.org/W4377865163","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3000197790","https://openalex.org/W4315865067","https://openalex.org/W2979433843"],"abstract_inverted_index":{"Our":[0,119],"KDDI":[1],"Research":[2],"team":[3],"proposes":[4],"an":[5],"ensemble":[6,89,162],"model":[7,90],"for":[8],"the":[9,22,50,55,66,71,110,114,122,142,149,167],"Sussex-Huawei":[10],"Locomotion-Transportation":[11],"(SHL)":[12],"recognition":[13],"challenge":[14],"using":[15,31,49,59],"GPS":[16,23],"data.":[17],"During":[18],"preprocessing,":[19],"we":[20,75],"corrected":[21],"dataset":[24],"that":[25,61,130,148],"contains":[26],"errors":[27],"and":[28,70,95,103,138,161,172],"missing":[29],"values":[30],"a":[32],"Kalman":[33],"smoother.":[34],"Since":[35],"this":[36],"smoother":[37],"can":[38,43],"be":[39,44],"processed":[40],"offline,":[41],"it":[42,131],"used":[45],"to":[46,165],"correct":[47],"backforward":[48],"recorded":[51],"future":[52],"locations":[53],"in":[54,65],"dataset.":[56],"Further,":[57],"by":[58,126],"features":[60,140,155],"have":[62],"similar":[63,157],"distributions":[64,158],"train":[67,171],"subject's":[68],"data":[69],"other":[72],"subjects'":[73],"datasets,":[74],"could":[76],"achieve":[77],"robust":[78],"feature":[79],"selection":[80,153],"across":[81,159],"multiple":[82],"subjects.":[83],"The":[84,106],"first":[85],"stage":[86,108],"of":[87,113,116,135,151,154,169],"our":[88,127],"employs":[91],"classical":[92],"machine":[93],"learning":[94],"deep":[96],"neural":[97],"network":[98],"approaches":[99],"independently,":[100],"specifically,":[101],"LightGBM":[102],"LSTM,":[104],"respectively.":[105],"second":[107],"calculates":[109],"weighted":[111],"average":[112],"outputs":[115],"both":[117,136,170],"approaches.":[118],"results":[120],"show":[121],"improved":[123],"accuracy":[124,168],"contributed":[125,164],"ensemble,":[128],"suggesting":[129],"effectively":[132],"makes":[133],"use":[134,150],"statistical":[137],"non-statistical":[139],"given":[141],"suitable":[143],"base":[144],"models.":[145],"We":[146],"confirmed":[147],"Kalman-smoother,":[152],"with":[156],"subjects":[160],"modeling":[163],"improving":[166],"validation":[173],"datasets.":[174]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}