{"id":"https://openalex.org/W4389544707","doi":"https://doi.org/10.1109/vtc2023-fall60731.2023.10333464","title":"Time-Series based Fall Detection in Two-Wheelers","display_name":"Time-Series based Fall Detection in Two-Wheelers","publication_year":2023,"publication_date":"2023-10-10","ids":{"openalex":"https://openalex.org/W4389544707","doi":"https://doi.org/10.1109/vtc2023-fall60731.2023.10333464"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2023-fall60731.2023.10333464","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vtc2023-fall60731.2023.10333464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)","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/A5066413936","display_name":"Sai Usha Goparaju","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sai Usha Goparaju","raw_affiliation_strings":["IIIT,Computer Systems Group,Hyderabad,India","Computer Systems Group, IIIT, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT,Computer Systems Group,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Computer Systems Group, IIIT, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093461100","display_name":"Keerthi Pothalaraju","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Keerthi Pothalaraju","raw_affiliation_strings":["IIIT,Computer Systems Group,Hyderabad,India","Computer Systems Group, IIIT, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT,Computer Systems Group,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Computer Systems Group, IIIT, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093461101","display_name":"Shriya Dullur","orcid":null},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shriya Dullur","raw_affiliation_strings":["IIIT,Computer Systems Group,Hyderabad,India","Computer Systems Group, IIIT, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT,Computer Systems Group,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Computer Systems Group, IIIT, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050760168","display_name":"Arihant Jain","orcid":"https://orcid.org/0000-0001-8813-4932"},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arihant Jain","raw_affiliation_strings":["IIIT,Computer Systems Group,Hyderabad,India","Computer Systems Group, IIIT, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT,Computer Systems Group,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Computer Systems Group, IIIT, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047166433","display_name":"Deepak Gangadharan","orcid":"https://orcid.org/0000-0001-6630-0012"},"institutions":[{"id":"https://openalex.org/I65181880","display_name":"Indian Institute of Technology Hyderabad","ror":"https://ror.org/01j4v3x97","country_code":"IN","type":"education","lineage":["https://openalex.org/I65181880"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepak Gangadharan","raw_affiliation_strings":["IIIT,Computer Systems Group,Hyderabad,India","Computer Systems Group, IIIT, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IIIT,Computer Systems Group,Hyderabad,India","institution_ids":["https://openalex.org/I65181880"]},{"raw_affiliation_string":"Computer Systems Group, IIIT, Hyderabad, India","institution_ids":["https://openalex.org/I65181880"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I65181880"],"apc_list":null,"apc_paid":null,"fwci":0.4356,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62243572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9993000030517578,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9943000078201294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6929752826690674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5977243781089783},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4332849085330963},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12304863333702087},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10654112696647644}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6929752826690674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5977243781089783},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4332849085330963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12304863333702087},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10654112696647644},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2023-fall60731.2023.10333464","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/vtc2023-fall60731.2023.10333464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.5099999904632568,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1974762721","https://openalex.org/W2018690964","https://openalex.org/W2086461263","https://openalex.org/W2159543516","https://openalex.org/W2408768806","https://openalex.org/W2623724154","https://openalex.org/W2754051771","https://openalex.org/W2793527938","https://openalex.org/W2898245143","https://openalex.org/W2973255917","https://openalex.org/W2979637742","https://openalex.org/W3167373986","https://openalex.org/W3206450379","https://openalex.org/W3217570383","https://openalex.org/W4256318381","https://openalex.org/W4379116143","https://openalex.org/W4385245566","https://openalex.org/W6739901393"],"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/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Driving":[0],"event":[1],"recognition":[2],"plays":[3],"a":[4,132],"crucial":[5],"role":[6],"in":[7,25,33,46,138,161],"understanding":[8,102],"and":[9,95],"enhancing":[10],"road":[11,159],"safety.":[12],"This":[13,142],"research":[14,143],"focuses":[15],"on":[16],"developing":[17],"efficient":[18],"time-series":[19,53],"based":[20,54,152],"models":[21,30,58],"for":[22,80,154],"Fall":[23,36,140,155],"detection":[24],"two-wheelers.":[26,165],"Traditional":[27],"machine":[28],"learning":[29,71],"proved":[31],"inadequate":[32],"accurately":[34],"classifying":[35],"scenarios":[37],"due":[38],"to":[39,42,88,114,145],"their":[40],"inability":[41],"capture":[43,89],"temporal":[44],"transitions":[45],"kinematic":[47],"states.":[48],"To":[49],"address":[50],"this":[51],"limitation,":[52],"Deep":[55],"Learning":[56],"(DL)":[57],"are":[59],"proposed,":[60],"utilizing":[61],"Long":[62],"Short-Term":[63],"Memory":[64],"(LSTM)":[65],"networks.":[66],"These":[67],"networks":[68],"enable":[69],"direct":[70],"from":[72,92],"raw":[73],"time":[74],"series":[75],"data,":[76],"eliminating":[77],"the":[78,100,121,146,162],"need":[79],"manual":[81],"feature":[82],"engineering.":[83],"Additionally,":[84],"Bi-LSTMs":[85],"were":[86],"employed":[87],"contextual":[90],"information":[91],"both":[93],"past":[94],"future":[96],"timesteps,":[97],"further":[98],"improving":[99],"model\u2019s":[101],"of":[103,129,135,148,164],"driving":[104],"events.":[105],"The":[106],"architecture":[107],"was":[108],"enhanced":[109],"with":[110,131],"an":[111,126,149],"attention":[112],"mechanism":[113],"boost":[115],"accuracy.":[116],"Experimental":[117],"results":[118],"showcased":[119],"that":[120],"proposed":[122],"Bi-LSTM":[123],"model":[124],"achieved":[125],"overall":[127],"accuracy":[128,134],"97%,":[130],"specific":[133],"approximately":[136],"92%":[137],"detecting":[139],"scenarios.":[141],"contributes":[144],"development":[147],"accurate":[150],"Time-series":[151],"system":[153],"detection,":[156],"facilitating":[157],"improved":[158],"safety":[160],"context":[163]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
