{"id":"https://openalex.org/W3161712680","doi":"https://doi.org/10.1145/3412382.3458785","title":"Non-parametric Bayesian Learning for Newcomer Detection using Footstep-Induced Floor Vibration","display_name":"Non-parametric Bayesian Learning for Newcomer Detection using Footstep-Induced Floor Vibration","publication_year":2021,"publication_date":"2021-05-10","ids":{"openalex":"https://openalex.org/W3161712680","doi":"https://doi.org/10.1145/3412382.3458785","mag":"3161712680"},"language":"en","primary_location":{"id":"doi:10.1145/3412382.3458785","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3412382.3458785","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3412382.3458785","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3412382.3458785","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065679179","display_name":"Yiwen Dong","orcid":"https://orcid.org/0000-0002-7877-1783"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiwen Dong","raw_affiliation_strings":["Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I27837315","https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004259911","display_name":"Jonathon Fagert","orcid":"https://orcid.org/0000-0001-7905-6987"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathon Fagert","raw_affiliation_strings":["Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I27837315","https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100415694","display_name":"Pei Zhang","orcid":"https://orcid.org/0000-0002-8512-1615"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pei Zhang","raw_affiliation_strings":["Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I27837315","https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082588269","display_name":"Hae Young Noh","orcid":"https://orcid.org/0000-0002-7998-3657"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hae Young Noh","raw_affiliation_strings":["Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, CA, Carnegie Mellon University, Pittsburgh, PA, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I27837315","https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2547,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.49020845,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"404","last_page":"405"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12740","display_name":"Gait Recognition and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.9987999796867371,"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"}},{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9933000206947327,"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/computer-science","display_name":"Computer science","score":0.6344431042671204},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5988019108772278},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5687769651412964},{"id":"https://openalex.org/keywords/headset","display_name":"Headset","score":0.5663673281669617},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.564135730266571},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.5481052398681641},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48052892088890076},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46029412746429443},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4301777482032776},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09832942485809326},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09084305167198181}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6344431042671204},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5988019108772278},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5687769651412964},{"id":"https://openalex.org/C2780657452","wikidata":"https://www.wikidata.org/wiki/Q1193170","display_name":"Headset","level":2,"score":0.5663673281669617},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.564135730266571},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.5481052398681641},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48052892088890076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46029412746429443},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4301777482032776},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09832942485809326},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09084305167198181},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3412382.3458785","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3412382.3458785","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3412382.3458785","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3412382.3458785","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3412382.3458785","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3412382.3458785","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th International Conference on Information Processing in Sensor Networks (co-located with CPS-IoT Week 2021)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7699999809265137}],"awards":[{"id":"https://openalex.org/G5450582826","display_name":null,"funder_award_id":"NSF-CMMI-2026699","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5476048196","display_name":null,"funder_award_id":"2026699","funder_id":"https://openalex.org/F4320337391","funder_display_name":"Division of Civil, Mechanical and Manufacturing Innovation"},{"id":"https://openalex.org/G7194142306","display_name":null,"funder_award_id":"CMMI-2026699","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7985986877","display_name":"CAREER: Structures as Sensors: Elder Activity Level Monitoring through Structural Vibrations","funder_award_id":"2026699","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3161712680.pdf","grobid_xml":"https://content.openalex.org/works/W3161712680.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W2341614611","https://openalex.org/W2756053574","https://openalex.org/W2786185471","https://openalex.org/W2932818989","https://openalex.org/W4251668937"],"related_works":["https://openalex.org/W2549567669","https://openalex.org/W2323345712","https://openalex.org/W104838418","https://openalex.org/W1532889363","https://openalex.org/W4292416459","https://openalex.org/W2394331374","https://openalex.org/W4225253169","https://openalex.org/W2626842453","https://openalex.org/W4200422973","https://openalex.org/W4233349824"],"abstract_inverted_index":{"Detecting":[0],"a":[1,88],"previously":[2],"unknown":[3],"person":[4],"(newcomer":[5],"detection)":[6],"is":[7],"critical":[8],"for":[9,61,108],"visitor":[10],"management,":[11],"intruder":[12],"prevention,":[13],"and":[14,72,101],"access":[15],"control":[16],"in":[17,69,79,125],"smart":[18],"buildings.":[19],"Biometrics":[20],"have":[21],"been":[22],"used":[23],"to":[24,52,65,122],"detect":[25],"newcomers,":[26],"including":[27],"face,":[28],"fingerprint,":[29],"voice,":[30],"iris,":[31],"etc.":[32],"These":[33],"approaches":[34],"often":[35],"require":[36,43],"active":[37],"participation":[38],"of":[39,76,103,131],"the":[40,66,73,84,99],"users":[41],"or":[42],"dense":[44],"instrumentation.":[45],"Prior":[46],"work":[47],"using":[48],"footstep-induced":[49,70,105],"floor":[50,106],"vibration":[51,107],"identify":[53],"people":[54,63,119],"removes":[55],"these":[56],"requirements,":[57],"but":[58],"only":[59],"functions":[60],"known":[62],"due":[64],"high":[67],"variability":[68,100],"vibrations":[71],"limited":[74],"number":[75],"predicted":[77],"classes":[78],"supervised":[80],"learning.":[81],"To":[82],"overcome":[83],"limitations,":[85],"we":[86],"introduce":[87],"newcomer":[89,109,126],"detection":[90,127],"system":[91],"based":[92],"on":[93],"non-parametric":[94],"Bayesian":[95],"learning,":[96],"which":[97],"models":[98],"distribution":[102],"consecutive":[104,133],"walking":[110],"patterns.":[111],"Preliminary":[112],"results":[113],"from":[114],"real-world":[115],"experiments":[116],"with":[117,128],"6":[118],"show":[120],"up":[121],"92%":[123],"accuracy":[124],"an":[129],"average":[130],"4":[132],"footsteps.":[134]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-10-10T00:00:00"}