{"id":"https://openalex.org/W2182955076","doi":"https://doi.org/10.1109/dsaa.2015.7344875","title":"A fusion-based approach for user activities recognition on smart phones","display_name":"A fusion-based approach for user activities recognition on smart phones","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2182955076","doi":"https://doi.org/10.1109/dsaa.2015.7344875","mag":"2182955076"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa.2015.7344875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","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/A5036808772","display_name":"Gunarto Sindoro Njoo","orcid":"https://orcid.org/0000-0002-4991-8491"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Gunarto Sindoro Njoo","raw_affiliation_strings":["National Chiao Tung University"],"affiliations":[{"raw_affiliation_string":"National Chiao Tung University","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007373984","display_name":"Xiao Wen Ruan","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Xiao Wen Ruan","raw_affiliation_strings":["National Chiao Tung University"],"affiliations":[{"raw_affiliation_string":"National Chiao Tung University","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039657695","display_name":"Kuo-Wei Hsu","orcid":"https://orcid.org/0000-0002-3496-5439"},"institutions":[{"id":"https://openalex.org/I87354575","display_name":"National Chengchi University","ror":"https://ror.org/03rqk8h36","country_code":"TW","type":"education","lineage":["https://openalex.org/I87354575"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Kuo-Wei Hsu","raw_affiliation_strings":["National Chengchi University"],"affiliations":[{"raw_affiliation_string":"National Chengchi University","institution_ids":["https://openalex.org/I87354575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102958591","display_name":"Wen-Chih Peng","orcid":"https://orcid.org/0000-0001-8964-0933"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen-Chih Peng","raw_affiliation_strings":["National Chiao Tung University"],"affiliations":[{"raw_affiliation_string":"National Chiao Tung University","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5036808772"],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":1.124,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.8513037,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994000196456909,"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/T11896","display_name":"Opportunistic and Delay-Tolerant Networks","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8376426696777344},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.741089403629303},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6189165115356445},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5622896552085876},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5144994258880615},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4443582594394684},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42442527413368225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41840609908103943},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.4180898368358612}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8376426696777344},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.741089403629303},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6189165115356445},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5622896552085876},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5144994258880615},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4443582594394684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42442527413368225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41840609908103943},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.4180898368358612},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa.2015.7344875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","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":24,"referenced_works":["https://openalex.org/W117434946","https://openalex.org/W1673310716","https://openalex.org/W1880262756","https://openalex.org/W1985697096","https://openalex.org/W1992905840","https://openalex.org/W2039557327","https://openalex.org/W2073021764","https://openalex.org/W2122516730","https://openalex.org/W2129619420","https://openalex.org/W2133990480","https://openalex.org/W2147880316","https://openalex.org/W2150882603","https://openalex.org/W2153207204","https://openalex.org/W2153635508","https://openalex.org/W2154718966","https://openalex.org/W2160642098","https://openalex.org/W2164297046","https://openalex.org/W2171805028","https://openalex.org/W3120421331","https://openalex.org/W4247105055","https://openalex.org/W6604760592","https://openalex.org/W6637131181","https://openalex.org/W6639619044","https://openalex.org/W6682082992"],"related_works":["https://openalex.org/W2281498195","https://openalex.org/W3195649134","https://openalex.org/W2506504620","https://openalex.org/W2017526120","https://openalex.org/W2610664080","https://openalex.org/W2188304107","https://openalex.org/W2055243143","https://openalex.org/W2892259437","https://openalex.org/W2944566775","https://openalex.org/W2345493171"],"abstract_inverted_index":{"In":[0,72,146],"the":[1,118,150,210],"recent":[2],"years,":[3],"several":[4,25],"research":[5],"works":[6],"have":[7],"been":[8],"conducted":[9,207],"on":[10,161,181],"collecting":[11],"context":[12],"data":[13,43,53,141,195],"from":[14,124,140],"various":[15],"sensors":[16],"for":[17,68,92,152],"activity":[18,93],"inference.":[19],"We":[20],"observe":[21],"that":[22,44,56,169],"users":[23],"perform":[24],"actions":[26,40],"in":[27,60],"their":[28],"mobile":[29],"phones:":[30],"taking":[31],"photos,":[32],"performing":[33],"check-ins,":[34],"and":[35,113,164],"accessing":[36],"Wi-Fi":[37],"networks.":[38,127],"These":[39],"generate":[41],"spatial-temporal":[42],"could":[45,54,81,185],"be":[46],"utilized":[47],"to":[48,76,136,148,173,208],"capture":[49],"user":[50,58,84,101,110,138,176,187],"activities.":[51,85],"Spatial-temporal":[52],"indicate":[55],"a":[57,61,65,69,100,109,122],"stays":[59],"certain":[62,70],"location":[63],"at":[64],"particular":[66],"time":[67],"activity.":[71],"addition,":[73,147],"by":[74],"referring":[75],"social":[77,126],"media":[78],"data,":[79],"one":[80],"also":[82,206],"infer":[83,137,175,186],"Three":[86],"types":[87],"of":[88,121,203],"features":[89],"are":[90],"extracted":[91],"inference:":[94],"1)":[95],"geographical":[96],"feature,":[97,106,116,154],"indicating":[98,107],"where":[99],"performs":[102,111],"activities;":[103,112],"2)":[104],"temporal":[105],"when":[108,190],"3)":[114],"semantic":[115,119],"showing":[117],"concept":[120],"place":[123],"location-based":[125],"Here,":[128],"we":[129,155],"propose":[130,157],"Spatial-Temporal":[131],"Activity":[132],"Inference":[133],"Model":[134],"(STAIM)":[135],"activities":[139,188],"with":[142],"those":[143],"three":[144,158],"features.":[145],"determine":[149],"weight":[151],"each":[153],"further":[156],"methods":[159],"based":[160],"frequency,":[162],"entropy,":[163],"entropy-frequency.":[165],"Experimental":[166],"results":[167],"show":[168],"STAIM":[170,184],"is":[171,192,205],"able":[172],"effectively":[174],"activities,":[177],"achieving":[178],"75%":[179],"accuracy":[180],"average.":[182],"Moreover,":[183,200],"even":[189],"there":[191],"no":[193],"training":[194],"(with":[196],"some":[197],"performance":[198],"loss).":[199],"sensitive":[201],"analysis":[202],"parameters":[204],"select":[209],"most":[211],"optimal":[212],"parameter.":[213]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}