{"id":"https://openalex.org/W4415539038","doi":"https://doi.org/10.1145/3746027.3755115","title":"Towards Consumer-Grade Cybersickness Prediction: Multi-Model Alignment for Real-Time Vision-Only Inference","display_name":"Towards Consumer-Grade Cybersickness Prediction: Multi-Model Alignment for Real-Time Vision-Only Inference","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415539038","doi":"https://doi.org/10.1145/3746027.3755115"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755115","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755115","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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":null,"display_name":"Yitong Zhu","orcid":"https://orcid.org/0009-0007-5717-3390"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yitong Zhu","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0007-5717-3390","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhuowen Liang","orcid":"https://orcid.org/0009-0001-0217-1685"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuowen Liang","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-0217-1685","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yiming Wu","orcid":"https://orcid.org/0009-0007-5119-8140"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yiming Wu","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0007-5119-8140","affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026759430","display_name":"Tangyao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tangyao Li","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0008-6157-9294","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100409330","display_name":"Yuyang Wang","orcid":"https://orcid.org/0000-0003-0242-8935"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuyang Wang","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0242-8935","affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6859","last_page":"6867"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.954200029373169,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.954200029373169,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9451000094413757,"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/T10648","display_name":"Virtual Reality Applications and Impacts","score":0.9290000200271606,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/virtual-reality","display_name":"Virtual reality","score":0.6126000285148621},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.603600025177002},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5533999800682068},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5515000224113464},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.5128999948501587},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.453900009393692},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40849998593330383},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.40459999442100525},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.3093999922275543}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8090999722480774},{"id":"https://openalex.org/C194969405","wikidata":"https://www.wikidata.org/wiki/Q170519","display_name":"Virtual reality","level":2,"score":0.6126000285148621},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.603600025177002},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5533999800682068},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5460000038146973},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.5128999948501587},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.453900009393692},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4291999936103821},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3937000036239624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3280999958515167},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.30660000443458557},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.29319998621940613},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.28459998965263367},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2840999960899353},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C11940443","wikidata":"https://www.wikidata.org/wiki/Q16965645","display_name":"Slowness","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2667999863624573},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755115","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755115","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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":22,"referenced_works":["https://openalex.org/W2054749727","https://openalex.org/W2342688137","https://openalex.org/W2415889196","https://openalex.org/W2791342729","https://openalex.org/W2921897326","https://openalex.org/W2968488180","https://openalex.org/W2989624678","https://openalex.org/W2999931550","https://openalex.org/W3027054797","https://openalex.org/W3111027453","https://openalex.org/W3134586965","https://openalex.org/W3162069348","https://openalex.org/W3162438733","https://openalex.org/W3208497351","https://openalex.org/W4309505617","https://openalex.org/W4320402145","https://openalex.org/W4386753231","https://openalex.org/W4396918816","https://openalex.org/W4402721814","https://openalex.org/W4403792038","https://openalex.org/W4404617010","https://openalex.org/W4409657399"],"related_works":[],"abstract_inverted_index":{"Cybersickness":[0],"remains":[1],"a":[2,49,78,85],"major":[3],"obstacle":[4],"to":[5,89,107],"the":[6,104,119],"widespread":[7],"adoption":[8],"of":[9,153],"immersive":[10],"virtual":[11],"reality":[12],"(VR),":[13],"particularly":[14],"in":[15],"consumer-grade":[16,164],"environments.":[17],"While":[18],"prior":[19],"methods":[20],"rely":[21],"on":[22],"invasive":[23],"signals":[24,60],"such":[25],"as":[26],"electroencephalography":[27],"(EEG)":[28],"for":[29,41,53,161],"high":[30],"predictive":[31],"accuracy,":[32,138],"these":[33],"approaches":[34,142],"require":[35],"specialized":[36],"hardware":[37],"and":[38,72],"are":[39],"impractical":[40],"real-world":[42],"applications.":[43],"In":[44],"this":[45],"work,":[46],"we":[47],"propose":[48],"scalable,":[50],"deployable":[51],"framework":[52,156],"personalized":[54,109],"cybersickness":[55,124],"prediction":[56],"leveraging":[57],"only":[58,126],"non-invasive":[59],"readily":[61],"available":[62],"from":[63],"commercial":[64],"VR":[65,165],"headsets,":[66],"including":[67],"head":[68],"motion,":[69],"eye":[70],"tracking,":[71],"physiological":[73],"responses.":[74],"Our":[75],"model":[76,120,135],"employs":[77],"modality-specific":[79],"graph":[80],"neural":[81],"network":[82],"enhanced":[83],"with":[84,115],"Difference":[86],"Attention":[87],"Module":[88],"extract":[90],"temporal-spatial":[91],"embeddings":[92],"capturing":[93],"dynamic":[94],"changes":[95],"across":[96],"modalities.":[97],"A":[98],"cross-modal":[99],"alignment":[100],"module":[101],"jointly":[102],"trains":[103],"video":[105,113,127],"encoder":[106],"learn":[108],"traits":[110],"by":[111],"aligning":[112],"features":[114],"sensor-derived":[116],"representations.":[117],"Consequently,":[118],"accurately":[121],"predicts":[122],"individual":[123],"using":[125],"input":[128],"during":[129],"inference.":[130],"Experimental":[131],"results":[132],"show":[133],"our":[134,155],"achieves":[136],"88.4%":[137],"closely":[139],"matching":[140],"EEG-based":[141],"(89.16%),":[143],"while":[144],"reducing":[145],"deployment":[146],"complexity.":[147],"With":[148],"an":[149],"average":[150],"inference":[151],"latency":[152],"90ms,":[154],"supports":[157],"real-time":[158],"applications,":[159],"ideal":[160],"integration":[162],"into":[163],"platforms":[166],"without":[167],"compromising":[168],"personalization":[169],"or":[170],"performance.":[171],"The":[172],"code":[173],"will":[174],"be":[175],"relesed":[176],"at":[177],"https://github.com/U235-Aurora/PTGNN.":[178]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-25T00:00:00"}
