{"id":"https://openalex.org/W4389666284","doi":"https://doi.org/10.1109/iros55552.2023.10342442","title":"The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI","display_name":"The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI","publication_year":2023,"publication_date":"2023-10-01","ids":{"openalex":"https://openalex.org/W4389666284","doi":"https://doi.org/10.1109/iros55552.2023.10342442"},"language":"en","primary_location":{"id":"doi:10.1109/iros55552.2023.10342442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros55552.2023.10342442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5056854596","display_name":"Alexandra Bremers","orcid":"https://orcid.org/0000-0001-5973-949X"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alexandra Bremers","raw_affiliation_strings":["Cornell Tech"],"affiliations":[{"raw_affiliation_string":"Cornell Tech","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030027463","display_name":"Maria Teresa Parreira","orcid":"https://orcid.org/0000-0001-6191-3127"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maria Teresa Parreira","raw_affiliation_strings":["Cornell Tech"],"affiliations":[{"raw_affiliation_string":"Cornell Tech","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000121526","display_name":"Xuanyu Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuanyu Fang","raw_affiliation_strings":["Cornell Tech"],"affiliations":[{"raw_affiliation_string":"Cornell Tech","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088914612","display_name":"Natalie Friedman","orcid":"https://orcid.org/0000-0003-4751-7739"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Natalie Friedman","raw_affiliation_strings":["Cornell Tech"],"affiliations":[{"raw_affiliation_string":"Cornell Tech","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083712092","display_name":"Adolfo Ramirez-Aristizabal","orcid":null},"institutions":[{"id":"https://openalex.org/I1310439424","display_name":"Accenture (Switzerland)","ror":"https://ror.org/041r3e346","country_code":"CH","type":"company","lineage":["https://openalex.org/I1310439424","https://openalex.org/I4210093804"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Adolfo Ramirez-Aristizabal","raw_affiliation_strings":["Accenture Labs"],"affiliations":[{"raw_affiliation_string":"Accenture Labs","institution_ids":["https://openalex.org/I1310439424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069980459","display_name":"Alexandria Pabst","orcid":"https://orcid.org/0000-0002-5130-5109"},"institutions":[{"id":"https://openalex.org/I1310439424","display_name":"Accenture (Switzerland)","ror":"https://ror.org/041r3e346","country_code":"CH","type":"company","lineage":["https://openalex.org/I1310439424","https://openalex.org/I4210093804"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Alexandria Pabst","raw_affiliation_strings":["Accenture Labs"],"affiliations":[{"raw_affiliation_string":"Accenture Labs","institution_ids":["https://openalex.org/I1310439424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078041406","display_name":"Mirjana Spasojevic","orcid":null},"institutions":[{"id":"https://openalex.org/I1310439424","display_name":"Accenture (Switzerland)","ror":"https://ror.org/041r3e346","country_code":"CH","type":"company","lineage":["https://openalex.org/I1310439424","https://openalex.org/I4210093804"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Mirjana Spasojevic","raw_affiliation_strings":["Accenture Labs"],"affiliations":[{"raw_affiliation_string":"Accenture Labs","institution_ids":["https://openalex.org/I1310439424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060062426","display_name":"Michael Kuniavsky","orcid":"https://orcid.org/0000-0001-6501-6749"},"institutions":[{"id":"https://openalex.org/I1310439424","display_name":"Accenture (Switzerland)","ror":"https://ror.org/041r3e346","country_code":"CH","type":"company","lineage":["https://openalex.org/I1310439424","https://openalex.org/I4210093804"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Michael Kuniavsky","raw_affiliation_strings":["Accenture Labs"],"affiliations":[{"raw_affiliation_string":"Accenture Labs","institution_ids":["https://openalex.org/I1310439424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016068576","display_name":"Wendy Ju","orcid":"https://orcid.org/0000-0002-3119-611X"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wendy Ju","raw_affiliation_strings":["Cornell Tech"],"affiliations":[{"raw_affiliation_string":"Cornell Tech","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5056854596"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":1.1887,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.78771552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"11443","last_page":"11450"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10809","display_name":"Occupational Health and Safety Research","score":0.9332000017166138,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10809","display_name":"Occupational Health and Safety Research","score":0.9332000017166138,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11357","display_name":"Risk and Safety Analysis","score":0.9160000085830688,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bystander-effect","display_name":"Bystander effect","score":0.9490139484405518},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.6734991669654846},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4598783552646637},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35129761695861816},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1453656554222107},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.07591244578361511},{"id":"https://openalex.org/keywords/communication","display_name":"Communication","score":0.057439953088760376}],"concepts":[{"id":"https://openalex.org/C19106626","wikidata":"https://www.wikidata.org/wiki/Q230749","display_name":"Bystander effect","level":2,"score":0.9490139484405518},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.6734991669654846},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4598783552646637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35129761695861816},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1453656554222107},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.07591244578361511},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.057439953088760376}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros55552.2023.10342442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros55552.2023.10342442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W1815317900","https://openalex.org/W1965696296","https://openalex.org/W1982867232","https://openalex.org/W2008933718","https://openalex.org/W2018582012","https://openalex.org/W2030540859","https://openalex.org/W2053154970","https://openalex.org/W2093451326","https://openalex.org/W2104999980","https://openalex.org/W2113057580","https://openalex.org/W2136058926","https://openalex.org/W2194775991","https://openalex.org/W2586877827","https://openalex.org/W2594447368","https://openalex.org/W2618530766","https://openalex.org/W2734759726","https://openalex.org/W2767580168","https://openalex.org/W3009763971","https://openalex.org/W3015360161","https://openalex.org/W3034630387","https://openalex.org/W3116020965","https://openalex.org/W3135854854","https://openalex.org/W3170074351","https://openalex.org/W3174226175","https://openalex.org/W3205843018","https://openalex.org/W4225517595","https://openalex.org/W4298112283","https://openalex.org/W4312472107","https://openalex.org/W4323870627","https://openalex.org/W6758460681","https://openalex.org/W6794087030","https://openalex.org/W6809937328","https://openalex.org/W6840240231","https://openalex.org/W6892105905"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2053004761","https://openalex.org/W2060232277","https://openalex.org/W2372296322","https://openalex.org/W2370719635","https://openalex.org/W2359636035","https://openalex.org/W2620667791","https://openalex.org/W2083162155"],"abstract_inverted_index":{"For":[0],"a":[1,15,58,76,86,115],"robot":[2,68,172],"to":[3,46,53,61,65,114,119,149,167],"repair":[4],"its":[5],"own":[6],"error,":[7],"it":[8,12],"must":[9],"first":[10],"know":[11],"has":[13],"made":[14],"mistake.":[16],"One":[17],"way":[18],"that":[19,39],"people":[20],"detect":[21,47,168],"errors":[22,169],"is":[23],"from":[24,28,95],"the":[25,100,103,108,183,193],"implicit":[26],"reactions":[27,94,152],"bystanders":[29],"-":[30],"their":[31],"confusion,":[32],"smirks,":[33],"or":[34],"giggles":[35],"clue":[36],"us":[37],"in":[38,163],"something":[40],"unexpected":[41],"occurred.":[42],"To":[43,98],"enable":[44],"robots":[45],"and":[48,67,80,129,146,153,156,170],"act":[49],"on":[50],"bystander":[51,63,109,151,190],"responses":[52,64],"task":[54,82],"failures,":[55,83],"we":[56,84,106,179],"developed":[57],"novel":[59],"method":[60],"elicit":[62],"human":[66,79,93],"errors.":[69],"Using":[70],"46":[71],"different":[72,125],"stimulus":[73],"videos":[74,91],"featuring":[75],"variety":[77],"of":[78,88,92,102,176,189,195],"machine":[81],"collected":[85,104],"total":[87],"2,452":[89],"webcam":[90],"54":[96],"participants.":[97],"test":[99],"viability":[101],"data,":[105],"used":[107,148,162],"reaction":[110],"dataset":[111,188],"as":[112],"input":[113],"deep-learning":[116],"model,":[117],"BADNet,":[118],"predict":[120,154],"failure":[121],"occurrence.":[122],"We":[123,140],"tested":[124],"data":[126],"labeling":[127],"methods":[128],"learned":[130],"how":[131,157],"they":[132],"affect":[133],"model":[134,150],"performance,":[135],"achieving":[136],"precisions":[137],"above":[138],"90%.":[139],"discuss":[141],"strategies":[142],"(manual":[143],"labelling,":[144],"failure-vs-control,":[145],"failure-time)":[147],"failure,":[155],"this":[158,177],"approach":[159],"can":[160],"be":[161],"real-world":[164],"robotic":[165],"deployments":[166],"improve":[171],"performance.":[173],"As":[174],"part":[175],"work,":[178],"also":[180],"contribute":[181],"with":[182],"\u201cBystander":[184],"Affect":[185],"Detection\u201d":[186],"(BAD)":[187],"reactions,":[191],"supporting":[192],"development":[194],"better":[196],"prediction":[197],"models.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
