{"id":"https://openalex.org/W2134426937","doi":"https://doi.org/10.1109/icassp.2009.4959522","title":"Acoustic fall detection using Gaussian mixture models and GMM supervectors","display_name":"Acoustic fall detection using Gaussian mixture models and GMM supervectors","publication_year":2009,"publication_date":"2009-04-01","ids":{"openalex":"https://openalex.org/W2134426937","doi":"https://doi.org/10.1109/icassp.2009.4959522","mag":"2134426937"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2009.4959522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4959522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","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/A5110376025","display_name":"Xiaodan Zhuang","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaodan Zhuang","raw_affiliation_strings":["Department of ECE, University of Illinois, Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of ECE, University of Illinois, Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101980929","display_name":"Jing Huang","orcid":"https://orcid.org/0009-0005-4243-9235"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Huang","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024184433","display_name":"Gerasimos Potamianos","orcid":"https://orcid.org/0000-0002-9833-7124"},"institutions":[{"id":"https://openalex.org/I4387152169","display_name":"Institute of Informatics & Telecommunications","ror":"https://ror.org/0396t6k89","country_code":null,"type":"facility","lineage":["https://openalex.org/I203474044","https://openalex.org/I4387152169"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]},{"id":"https://openalex.org/I203474044","display_name":"National Centre of Scientific Research \"Demokritos\"","ror":"https://ror.org/038jp4m40","country_code":"GR","type":"facility","lineage":["https://openalex.org/I203474044"]}],"countries":["GR","US"],"is_corresponding":false,"raw_author_name":"Gerasimos Potamianos","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","National Centre of Scientific Research Demokritos, Institute of Informatics and Telecommunications, Athens, Greece"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"National Centre of Scientific Research Demokritos, Institute of Informatics and Telecommunications, Athens, Greece","institution_ids":["https://openalex.org/I203474044","https://openalex.org/I4387152169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004778663","display_name":"Mark Hasegawa\u2010Johnson","orcid":"https://orcid.org/0000-0002-5631-2893"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Hasegawa-Johnson","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110376025"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":9.4842,"has_fulltext":false,"cited_by_count":139,"citation_normalized_percentile":{"value":0.9804457,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"69","last_page":"72"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"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":0.9991000294685364,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9986000061035156,"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/T10860","display_name":"Speech and Audio Processing","score":0.9983999729156494,"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/mixture-model","display_name":"Mixture model","score":0.8126159906387329},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7257963418960571},{"id":"https://openalex.org/keywords/microphone","display_name":"Microphone","score":0.6286253929138184},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.6128515005111694},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6093400716781616},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5953310132026672},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.593314528465271},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.592528223991394},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5532481074333191},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5158409476280212},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46790680289268494},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4509451389312744},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4304963946342468},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.417610764503479},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15955498814582825}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.8126159906387329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7257963418960571},{"id":"https://openalex.org/C2778263558","wikidata":"https://www.wikidata.org/wiki/Q46384","display_name":"Microphone","level":3,"score":0.6286253929138184},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.6128515005111694},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6093400716781616},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5953310132026672},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.593314528465271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.592528223991394},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5532481074333191},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5158409476280212},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46790680289268494},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4509451389312744},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4304963946342468},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.417610764503479},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15955498814582825},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C68115822","wikidata":"https://www.wikidata.org/wiki/Q1068172","display_name":"Sound pressure","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp.2009.4959522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4959522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.141.3950","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.3950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.isle.uiuc.edu/pubs/2009/icassp09zhuang.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.294.5096","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.5096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ifp.illinois.edu/speech/pubs/2009/icassp09zhuang.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4000000059604645,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1567205956","https://openalex.org/W1930442888","https://openalex.org/W2018282406","https://openalex.org/W2084161055","https://openalex.org/W2105068979","https://openalex.org/W2114093941","https://openalex.org/W2128437937","https://openalex.org/W2133214879","https://openalex.org/W2147147599","https://openalex.org/W2153635508","https://openalex.org/W2170672895","https://openalex.org/W2625316144","https://openalex.org/W3215500402","https://openalex.org/W6633930718","https://openalex.org/W6675872639"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2942366970","https://openalex.org/W2807634898","https://openalex.org/W67128905","https://openalex.org/W2162449135","https://openalex.org/W1519654189","https://openalex.org/W2184687474"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,24,40,62,82,107,138,149],"system":[3,30],"that":[4,95],"detects":[5],"human":[6,85],"falls":[7,74],"in":[8],"the":[9,20,50,91,96,116],"home":[10],"environment,":[11],"distinguishing":[12],"them":[13],"from":[14,23,104],"competing":[15],"noise,":[16],"by":[17,37,137,154],"using":[18],"only":[19],"audio":[21,54,71,123],"signal":[22],"single":[25],"far-field":[26],"microphone.":[27],"The":[28,111],"proposed":[29],"models":[31],"each":[32],"fall":[33,99,119,150],"or":[34],"noise":[35],"segment":[36,124],"means":[38],"of":[39,78,84,90,106],"Gaussian":[41],"mixture":[42],"model":[43],"(GMM)":[44],"supervector,":[45],"whose":[46],"Euclidean":[47],"distance":[48],"measures":[49],"pairwise":[51],"difference":[52],"between":[53,64],"segments.":[55],"A":[56],"support":[57],"vector":[58],"machine":[59],"built":[60],"on":[61,81,143],"kernel":[63],"GMM":[65,109],"supervectors":[66],"is":[67],"employed":[68],"to":[69,102,132],"classify":[70],"segments":[72,135],"into":[73],"and":[75],"various":[76],"types":[77],"noise.":[79],"Experiments":[80],"dataset":[83],"falls,":[86],"collected":[87],"as":[88],"part":[89],"Netcarity":[92],"project,":[93],"show":[94],"method":[97],"improves":[98,148],"classification":[100],"F-score":[101],"67%":[103],"59%":[105],"baseline":[108],"classifier.":[110],"approach":[112],"also":[113],"effectively":[114],"addresses":[115],"more":[117],"difficult":[118],"detection":[120,151],"problem,":[121],"where":[122],"boundaries":[125],"are":[126],"unknown.":[127],"Specifically,":[128],"we":[129],"employ":[130],"it":[131],"reclassify":[133],"confusable":[134],"produced":[136],"dynamic":[139],"programming":[140],"scheme":[141],"based":[142],"traditional":[144],"GMMs.":[145],"Such":[146],"post-processing":[147],"accuracy":[152],"metric":[153],"5%":[155],"relative.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":15},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":8}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}