{"id":"https://openalex.org/W4366250340","doi":"https://doi.org/10.1109/percom56429.2023.10099215","title":"HeadMon: Head Dynamics Enabled Riding Maneuver Prediction","display_name":"HeadMon: Head Dynamics Enabled Riding Maneuver Prediction","publication_year":2023,"publication_date":"2023-03-13","ids":{"openalex":"https://openalex.org/W4366250340","doi":"https://doi.org/10.1109/percom56429.2023.10099215"},"language":"en","primary_location":{"id":"doi:10.1109/percom56429.2023.10099215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom56429.2023.10099215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5008592483","display_name":"Zengyi Han","orcid":"https://orcid.org/0000-0003-1250-3142"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zengyi Han","raw_affiliation_strings":["The University of Tokyo,Tokyo,Japan","The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101792857","display_name":"Liqiang Xu","orcid":"https://orcid.org/0000-0003-1480-7561"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Liqiang Xu","raw_affiliation_strings":["The University of Tokyo,Tokyo,Japan","The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081518832","display_name":"Xuefu Dong","orcid":"https://orcid.org/0009-0000-6164-1539"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xuefu Dong","raw_affiliation_strings":["The University of Tokyo,Tokyo,Japan","The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046878953","display_name":"Yuuki Nishiyama","orcid":"https://orcid.org/0000-0002-5549-5595"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuuki Nishiyama","raw_affiliation_strings":["The University of Tokyo,Tokyo,Japan","The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050720322","display_name":"Kaoru Sezaki","orcid":"https://orcid.org/0000-0003-1194-4632"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kaoru Sezaki","raw_affiliation_strings":["The University of Tokyo,Tokyo,Japan","The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":3.4838,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.948562,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"22","last_page":"31"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10370","display_name":"Traffic and Road Safety","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10525","display_name":"Human-Automation Interaction and Safety","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5723420977592468},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.4734589457511902},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.4381183385848999},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3989892303943634},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.35357293486595154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3049123287200928}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5723420977592468},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.4734589457511902},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.4381183385848999},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3989892303943634},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.35357293486595154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3049123287200928}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/percom56429.2023.10099215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percom56429.2023.10099215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1964232317","https://openalex.org/W2009193978","https://openalex.org/W2016654760","https://openalex.org/W2018690964","https://openalex.org/W2056978120","https://openalex.org/W2086755418","https://openalex.org/W2151226872","https://openalex.org/W2176240313","https://openalex.org/W2546764682","https://openalex.org/W2558581619","https://openalex.org/W2783437277","https://openalex.org/W2799604656","https://openalex.org/W2903744126","https://openalex.org/W2950412189","https://openalex.org/W2964344294","https://openalex.org/W2970913305","https://openalex.org/W2996962164","https://openalex.org/W3036171677","https://openalex.org/W3095845482","https://openalex.org/W3199972400","https://openalex.org/W4200098409","https://openalex.org/W4206083335","https://openalex.org/W4281705671"],"related_works":["https://openalex.org/W2091018038","https://openalex.org/W2225378543","https://openalex.org/W9839718","https://openalex.org/W3110613631","https://openalex.org/W4287122200","https://openalex.org/W2742744817","https://openalex.org/W2040913503","https://openalex.org/W3166845860","https://openalex.org/W3202472720","https://openalex.org/W2393010104"],"abstract_inverted_index":{"Although":[0],"micro-mobility":[1],"brings":[2],"convenience":[3],"to":[4,57,81,131,202],"modern":[5],"cities,":[6],"they":[7],"also":[8,184],"cause":[9],"various":[10],"social":[11],"problems,":[12],"such":[13],"as":[14,77,123,142],"traffic":[15],"accidents,":[16],"casualties,":[17],"and":[18,53,206],"substantial":[19],"economic":[20],"losses.":[21],"Wearing":[22],"protective":[23],"equipment":[24],"has":[25],"become":[26],"the":[27,38,45,64,73,83,101,114,118,124,127,136,153,156],"primary":[28],"recommendation":[29],"for":[30,50,96,173],"safe":[31,97],"riding.":[32,98,210],"However,":[33],"passive":[34],"protection":[35,52],"cannot":[36],"prevent":[37],"occurrence":[39],"of":[40,104,126,145,159,169,198],"accidents.":[41],"Thus,":[42],"timely":[43],"predicting":[44],"rider's":[46,74,84],"maneuver":[47,93,174],"is":[48],"essential":[49],"active":[51],"providing":[54],"more":[55,208],"time":[56,180],"avoid":[58],"potential":[59],"accidents":[60],"from":[61],"happening.":[62],"Through":[63,147],"qualitative":[65],"study,":[66],"we":[67,70],"argue":[68],"that":[69],"can":[71],"use":[72],"head":[75,102,120],"dynamic":[76],"an":[78,109,166],"information":[79],"source":[80],"predict":[82],"following":[85],"maneuvers.":[86],"We":[87,134],"accordingly":[88],"present":[89],"HeadMon,":[90],"a":[91,105,143,177,187,191],"riding":[92],"prediction":[94,175,179],"system":[95],"HeadMon":[99,137,199],"utilizes":[100],"dynamics":[103,121],"rider":[106],"by":[107],"installing":[108],"inertial":[110],"measurement":[111],"unit":[112],"on":[113,139],"helmet.":[115],"It":[116],"uses":[117],"extracted":[119],"features":[122],"input":[125],"deep":[128],"learning":[129],"architecture":[130],"achieve":[132,165],"prediction.":[133],"implemented":[135],"prototype":[138],"Android":[140],"smartphone":[141],"proof":[144],"concept.":[146],"comprehensive":[148],"experiments":[149],"with":[150],"20":[151],"participants,":[152],"result":[154],"demonstrates":[155],"excellent":[157],"performance":[158],"HeadMon:":[160],"not":[161],"only":[162],"could":[163,185],"it":[164,183,201],"overall":[167],"precision":[168],"at":[170],"least":[171],"85%":[172],"under":[176,190],"4s":[178],"gap,":[181],"but":[182],"keep":[186],"high":[188],"accuracy":[189],"low":[192],"sampling":[193],"rate.":[194],"The":[195],"low-cost":[196],"feature":[197],"allows":[200],"be":[203],"readily":[204],"deployable":[205],"towards":[207],"safety":[209]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}