{"id":"https://openalex.org/W4415065066","doi":"https://doi.org/10.1145/3716553.3750808","title":"BiFuseNet: A Multimodal Network for Estimating Blood Alcohol Concentration via Bidirectional Hierarchical Fusion","display_name":"BiFuseNet: A Multimodal Network for Estimating Blood Alcohol Concentration via Bidirectional Hierarchical Fusion","publication_year":2025,"publication_date":"2025-10-11","ids":{"openalex":"https://openalex.org/W4415065066","doi":"https://doi.org/10.1145/3716553.3750808"},"language":"en","primary_location":{"id":"doi:10.1145/3716553.3750808","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3716553.3750808","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3716553.3750808","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063183388","display_name":"Abdullah Tariq","orcid":null},"institutions":[{"id":"https://openalex.org/I12079687","display_name":"Edith Cowan University","ror":"https://ror.org/05jhnwe22","country_code":"AU","type":"education","lineage":["https://openalex.org/I12079687"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Abdullah Tariq","raw_affiliation_strings":["School of Science, Edith Cowan University, Perth, Australia"],"raw_orcid":"https://orcid.org/0000-0002-4943-5182","affiliations":[{"raw_affiliation_string":"School of Science, Edith Cowan University, Perth, Australia","institution_ids":["https://openalex.org/I12079687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088277173","display_name":"Arooba Maqsood","orcid":null},"institutions":[{"id":"https://openalex.org/I12079687","display_name":"Edith Cowan University","ror":"https://ror.org/05jhnwe22","country_code":"AU","type":"education","lineage":["https://openalex.org/I12079687"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Arooba Maqsood","raw_affiliation_strings":["Edith Cowan University, Joondalup, Western Australia, Australia"],"raw_orcid":"https://orcid.org/0009-0001-0853-9268","affiliations":[{"raw_affiliation_string":"Edith Cowan University, Joondalup, Western Australia, Australia","institution_ids":["https://openalex.org/I12079687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044828278","display_name":"Martin Ma\u0161ek","orcid":"https://orcid.org/0000-0001-8620-6779"},"institutions":[{"id":"https://openalex.org/I12079687","display_name":"Edith Cowan University","ror":"https://ror.org/05jhnwe22","country_code":"AU","type":"education","lineage":["https://openalex.org/I12079687"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Martin Masek","raw_affiliation_strings":["Edith Cowan University, Perth, Western Australia, Australia"],"raw_orcid":"https://orcid.org/0000-0001-8620-6779","affiliations":[{"raw_affiliation_string":"Edith Cowan University, Perth, Western Australia, Australia","institution_ids":["https://openalex.org/I12079687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075172808","display_name":"Syed Zulqarnain Gilani","orcid":"https://orcid.org/0000-0002-7448-2327"},"institutions":[{"id":"https://openalex.org/I12079687","display_name":"Edith Cowan University","ror":"https://ror.org/05jhnwe22","country_code":"AU","type":"education","lineage":["https://openalex.org/I12079687"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Syed Zulqarnain Gilani","raw_affiliation_strings":["Edith Cowan University, Perth, Australia"],"raw_orcid":"https://orcid.org/0000-0002-7448-2327","affiliations":[{"raw_affiliation_string":"Edith Cowan University, Perth, Australia","institution_ids":["https://openalex.org/I12079687"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I12079687"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13037477,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"605","last_page":"613"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9853000044822693,"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":0.9853000044822693,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9686999917030334,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9677000045776367,"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/sobriety","display_name":"Sobriety","score":0.6653000116348267},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6055999994277954},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5464000105857849},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5015000104904175},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.45730000734329224},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42649999260902405},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.41519999504089355},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4142000079154968}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7656000256538391},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7355999946594238},{"id":"https://openalex.org/C2777006689","wikidata":"https://www.wikidata.org/wiki/Q1791388","display_name":"Sobriety","level":2,"score":0.6653000116348267},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6055999994277954},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5015000104904175},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45579999685287476},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C2776610969","wikidata":"https://www.wikidata.org/wiki/Q205972","display_name":"Alcohol intoxication","level":4,"score":0.3968999981880188},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.38929998874664307},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3328000009059906},{"id":"https://openalex.org/C2994312012","wikidata":"https://www.wikidata.org/wiki/Q886470","display_name":"Blood alcohol","level":4,"score":0.31150001287460327},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3716553.3750808","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3716553.3750808","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th International Conference on Multimodal Interaction","raw_type":"proceedings-article"},{"id":"pmh:oai:ro.ecu.edu.au:ecuworks2022-2026-8050","is_oa":true,"landing_page_url":"https://ro.ecu.edu.au/ecuworks2022-2026/7050","pdf_url":null,"source":{"id":"https://openalex.org/S2765015692","display_name":"Australasian Journal of Paramedicine","issn_l":"2202-7270","issn":["2202-7270"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research outputs 2022 to 2026","raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3716553.3750808","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3716553.3750808","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W2009587806","https://openalex.org/W2194775991","https://openalex.org/W2341528187","https://openalex.org/W2531409750","https://openalex.org/W2892233757","https://openalex.org/W2902298447","https://openalex.org/W2943546688","https://openalex.org/W2963324517","https://openalex.org/W2963956866","https://openalex.org/W2967324759","https://openalex.org/W3005254922","https://openalex.org/W3042738572","https://openalex.org/W3045616224","https://openalex.org/W3049558495","https://openalex.org/W3093558599","https://openalex.org/W3101998545","https://openalex.org/W3105073457","https://openalex.org/W3118479569","https://openalex.org/W3125723743","https://openalex.org/W3128797422","https://openalex.org/W3130522242","https://openalex.org/W3131825083","https://openalex.org/W3139422143","https://openalex.org/W3157986864","https://openalex.org/W3194188852","https://openalex.org/W3204266507","https://openalex.org/W3208945181","https://openalex.org/W4225934279","https://openalex.org/W4285209723","https://openalex.org/W4295193372","https://openalex.org/W4296493373","https://openalex.org/W4306832756","https://openalex.org/W4306933361","https://openalex.org/W4310018131","https://openalex.org/W4312633740","https://openalex.org/W4317940151","https://openalex.org/W4379209584","https://openalex.org/W4385767168","https://openalex.org/W4386075510","https://openalex.org/W4388208917","https://openalex.org/W4389667009","https://openalex.org/W4390929373","https://openalex.org/W4391568236","https://openalex.org/W4391685585","https://openalex.org/W4394593178","https://openalex.org/W4401437240","https://openalex.org/W4403488533","https://openalex.org/W4406272728","https://openalex.org/W4409284206"],"related_works":[],"abstract_inverted_index":{"Drunk":[0],"driving":[1],"remains":[2],"a":[3,22,49,96,145,160,173,184],"significant":[4],"public":[5,161],"safety":[6],"challenge,":[7],"demanding":[8],"innovative":[9],"alternatives":[10],"to":[11,34,55],"conventional":[12],"methods":[13],"such":[14],"as":[15],"field":[16],"sobriety":[17],"tests":[18],"and":[19,37,65,89,116,130,139,168,178],"breathalysers.":[20],"Estimating":[21],"driver's":[23],"level":[24],"of":[25,40,110,124,176,181,187],"intoxication":[26],"through":[27],"facial":[28,79,92,137,141],"cues":[29,86],"is":[30],"particularly":[31],"challenging":[32],"due":[33],"the":[35,188],"subtle":[36],"person-specific":[38],"nature":[39],"alcohol-induced":[41],"behaviours.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46,143],"present":[47],"BiFuseNet,":[48,112],"3D":[50],"spatio-temporal":[51],"multi-modal":[52],"network":[53],"designed":[54],"classify":[56],"alcohol":[57,193],"impairment":[58],"levels":[59,109],"into":[60],"three":[61],"categories:":[62],"sober,":[63],"moderate,":[64],"severe.":[66],"Unlike":[67],"prior":[68],"approaches":[69],"that":[70,102,149,164],"rely":[71],"on":[72,159],"either":[73],"uni-modal":[74,167],"RGB":[75,88],"video":[76],"or":[77],"hand-crafted":[78],"features,":[80],"our":[81,111],"method":[82],"exploits":[83],"complementary":[84],"physiological":[85],"from":[87],"infrared":[90],"(IR)":[91],"videos.":[93],"We":[94],"introduce":[95],"Bi-directional":[97],"Hierarchical":[98],"Fusion":[99],"(BiHF)":[100],"module":[101],"applies":[103],"cross-attention":[104],"mechanisms":[105],"at":[106],"multiple":[107],"semantic":[108],"including":[113],"early,":[114],"middle,":[115],"late":[117],"feature":[118],"stages.":[119],"This":[120],"enables":[121],"deep":[122],"integration":[123],"modality-specific":[125],"signals":[126],"across":[127,154],"varying":[128],"temporal":[129],"spatial":[131],"contexts.":[132],"To":[133],"capture":[134],"both":[135],"short-term":[136],"movements":[138],"sustained":[140],"dynamics,":[142],"implement":[144],"sliding":[146],"window":[147],"strategy":[148],"samples":[150],"over":[151],"30":[152],"frames":[153],"ten-minute":[155],"recordings.":[156],"Extensive":[157],"experiments":[158],"dataset":[162],"demonstrate":[163],"BiFuseNet":[165],"outperforms":[166],"traditional":[169],"fusion":[170],"baselines,":[171],"achieving":[172],"classification":[174],"accuracy":[175],"88.41%":[177],"an":[179],"AUC-ROC":[180],"0.91,":[182],"establishing":[183],"new":[185],"state":[186],"art":[189],"in":[190],"estimating":[191],"blood":[192],"concentration.":[194]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-11T00:00:00"}
