{"id":"https://openalex.org/W4400727243","doi":"https://doi.org/10.1109/rose62198.2024.10591096","title":"Privacy-Secured Early Detection of Smartphone Users in Danger at Stations Using Depth Sensor and Deep Learning","display_name":"Privacy-Secured Early Detection of Smartphone Users in Danger at Stations Using Depth Sensor and Deep Learning","publication_year":2024,"publication_date":"2024-06-20","ids":{"openalex":"https://openalex.org/W4400727243","doi":"https://doi.org/10.1109/rose62198.2024.10591096"},"language":"en","primary_location":{"id":"doi:10.1109/rose62198.2024.10591096","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/rose62198.2024.10591096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","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/A5111259847","display_name":"Hiroto Obata","orcid":null},"institutions":[{"id":"https://openalex.org/I171481255","display_name":"Shibaura Institute of Technology","ror":"https://ror.org/020wjcq07","country_code":"JP","type":"education","lineage":["https://openalex.org/I171481255"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hiroto Obata","raw_affiliation_strings":["School of Engineering, Shibaura Institute of Technology,Dept. of Advanced Electronic Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"School of Engineering, Shibaura Institute of Technology,Dept. of Advanced Electronic Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I171481255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000630426","display_name":"Chinthaka Premachandra","orcid":"https://orcid.org/0000-0002-5775-5047"},"institutions":[{"id":"https://openalex.org/I171481255","display_name":"Shibaura Institute of Technology","ror":"https://ror.org/020wjcq07","country_code":"JP","type":"education","lineage":["https://openalex.org/I171481255"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chinthaka Premachandra","raw_affiliation_strings":["School of Engineering, Shibaura Institute of Technology,Dept. of Advanced Electronic Engineering,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"School of Engineering, Shibaura Institute of Technology,Dept. of Advanced Electronic Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I171481255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111259847"],"corresponding_institution_ids":["https://openalex.org/I171481255"],"apc_list":null,"apc_paid":null,"fwci":1.3479,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.8224157,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.8715999722480774,"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":0.8715999722480774,"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/T11800","display_name":"User Authentication and Security Systems","score":0.8252000212669373,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8111000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6396342515945435},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5114218592643738},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.5112874507904053},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.4957205057144165},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24155595898628235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6396342515945435},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5114218592643738},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5112874507904053},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.4957205057144165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24155595898628235}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rose62198.2024.10591096","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/rose62198.2024.10591096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","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":18,"referenced_works":["https://openalex.org/W1475617732","https://openalex.org/W1903127635","https://openalex.org/W2010340098","https://openalex.org/W2085261163","https://openalex.org/W2113325037","https://openalex.org/W2200528286","https://openalex.org/W2559085405","https://openalex.org/W2560609797","https://openalex.org/W2733443012","https://openalex.org/W2965132998","https://openalex.org/W2979750740","https://openalex.org/W3097096317","https://openalex.org/W3126380445","https://openalex.org/W3177008250","https://openalex.org/W4206826960","https://openalex.org/W4385151896","https://openalex.org/W4386920225","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"The":[0],"installation":[1],"of":[2,20,36,60,69,96,108,145,185,221,235,249],"platform":[3,64],"doors":[4],"at":[5,106,182],"train":[6],"stations":[7,37],"has":[8,52,75,92],"been":[9,53],"widely":[10],"adopted":[11],"as":[12],"a":[13,54,199,262],"preventative":[14],"measure":[15],"against":[16],"the":[17,49,58,67,94,137,143,146,170,194,208,219,228,233,236,247,256],"increasing":[18],"incidents":[19],"falls":[21,70],"from":[22,118],"station":[23],"platforms.":[24],"Despite":[25],"this":[26,86,242],"safety":[27],"initiative,":[28],"financial":[29],"constraints":[30],"mean":[31],"that":[32,162,241],"only":[33,245],"approximately":[34],"10%":[35],"in":[38,57,155,160,175,217],"countries":[39],"like":[40,165],"Japan":[41],"are":[42],"currently":[43],"equipped":[44],"with":[45],"these":[46],"doors.":[47],"Over":[48],"years,":[50],"there":[51],"noticeable":[55],"reduction":[56],"number":[59,220],"accidents":[61,186],"related":[62],"to":[63,72,85,100,133,188],"falls;":[65],"however,":[66],"incidence":[68],"attributed":[71],"smartphone":[73,104,177],"usage":[74],"remained":[76],"consistently":[77],"high,":[78],"without":[79,231],"any":[80],"significant":[81],"reduction.":[82],"In":[83],"response":[84],"persistent":[87],"issue,":[88],"our":[89,203],"research":[90],"team":[91],"investigated":[93],"potential":[95],"utilizing":[97],"depth":[98],"cameras":[99],"detect":[101],"and":[102,115,123,211],"alert":[103],"users":[105,178],"risk":[107,184],"accidents.":[109],"To":[110],"achieve":[111],"this,":[112],"we":[113,192],"gathered":[114],"analyzed":[116],"data":[117,161],"both":[119],"individuals":[120,252],"using":[121],"smartphones":[122],"those":[124],"who":[125,179],"were":[126],"not,":[127],"employing":[128],"advanced":[129],"deep":[130],"learning":[131,195],"techniques":[132],"accurately":[134],"distinguish":[135],"between":[136],"two":[138],"groups.":[139],"Our":[140,238],"approach":[141,243],"leveraged":[142],"capabilities":[144],"Dynamic":[147],"Graph":[148],"Convolutional":[149],"Neural":[150],"Network":[151],"(DGCNN),":[152],"which":[153,205],"excels":[154],"capturing":[156],"intricate":[157],"local":[158],"patterns":[159],"traditional":[163],"models":[164],"PointNet":[166],"often":[167],"miss.":[168],"Remarkably,":[169],"DGCNN":[171],"demonstrated":[172],"high":[173],"precision":[174],"identifying":[176],"might":[180],"be":[181],"greater":[183],"due":[187],"their":[189],"distraction.":[190],"Moreover,":[191],"optimized":[193],"process":[196,230],"by":[197],"incorporating":[198],"downsampling":[200],"technique":[201],"into":[202],"methodology,":[204],"significantly":[206],"enhanced":[207],"model's":[209],"efficiency":[210],"accuracy.":[212],"This":[213],"was":[214],"particularly":[215],"effective":[216],"reducing":[218],"training":[222,229,258],"epochs":[223],"needed,":[224],"thereby":[225],"speeding":[226],"up":[227],"compromising":[232],"quality":[234],"results.":[237],"findings":[239],"confirmed":[240],"not":[244],"improves":[246],"accuracy":[248],"detecting":[250],"at-risk":[251],"but":[253],"also":[254],"reduces":[255],"overall":[257],"time,":[259],"making":[260],"it":[261],"practical":[263],"solution":[264],"for":[265],"real-world":[266],"applications.":[267]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
