{"id":"https://openalex.org/W7165355512","doi":"https://doi.org/10.1145/3765611.3815496","title":"Sensing social interactions in buildings using ambient sensors: A chamber study","display_name":"Sensing social interactions in buildings using ambient sensors: A chamber study","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7165355512","doi":"https://doi.org/10.1145/3765611.3815496"},"language":null,"primary_location":{"id":"doi:10.1145/3765611.3815496","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3765611.3815496","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 2026 ACM Sustainability Week","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3765611.3815496","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100355128","display_name":"Shiyu Liu","orcid":"https://orcid.org/0000-0003-0208-8635"},"institutions":[{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Shiyu Liu","raw_affiliation_strings":["ETHOS Lab, EPFL, Fribourg, Switzerland"],"raw_orcid":"https://orcid.org/0009-0006-0803-1815","affiliations":[{"raw_affiliation_string":"ETHOS Lab, EPFL, Fribourg, Switzerland","institution_ids":["https://openalex.org/I5124864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056589095","display_name":"Andrew Sonta","orcid":"https://orcid.org/0000-0001-9021-0842"},"institutions":[{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Andrew Sonta","raw_affiliation_strings":["ETHOS Lab, EPFL, Fribourg, Switzerland"],"raw_orcid":"https://orcid.org/0000-0001-9021-0842","affiliations":[{"raw_affiliation_string":"ETHOS Lab, EPFL, Fribourg, Switzerland","institution_ids":["https://openalex.org/I5124864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I5124864"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.83497402,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"485","last_page":"492"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.2727000117301941,"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":0.2727000117301941,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.260699987411499,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0364999994635582,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.2745000123977661},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.2574000060558319},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.24009999632835388},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.23899999260902405}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4047999978065491},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4002000093460083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39089998602867126},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28360000252723694},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.24009999632835388},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.23899999260902405},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.23119999468326569}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3765611.3815496","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3765611.3815496","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 2026 ACM Sustainability Week","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3765611.3815496","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3765611.3815496","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 2026 ACM Sustainability Week","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5822546482086182,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1984515777","https://openalex.org/W2020020887","https://openalex.org/W2061656867","https://openalex.org/W2487043258","https://openalex.org/W2599103181","https://openalex.org/W2738662141","https://openalex.org/W2888364758","https://openalex.org/W2921493711","https://openalex.org/W3123877353","https://openalex.org/W4292641593","https://openalex.org/W4323360378","https://openalex.org/W4385169374","https://openalex.org/W4385617616","https://openalex.org/W4400581949","https://openalex.org/W7153739497"],"related_works":[],"abstract_inverted_index":{"Buildings":[0],"bear":[1],"the":[2,99,116,121,184],"dual":[3],"responsibility":[4],"of":[5,64,101,186],"achieving":[6,128],"environmental":[7,42],"sustainability":[8],"and":[9,39,67,74,81,94,97,105,138,198],"supporting":[10],"social":[11,16,26,35,126,212],"value.":[12],"Accurately":[13],"sensing":[14,34,196,203],"indoor":[15,41],"interactions,":[17],"particularly":[18],"verbal":[19],"conversations,":[20],"is":[21],"key":[22],"to":[23,60,182],"quantifying":[24],"spatial":[25,95],"vibrancy.":[27],"This":[28],"study":[29,49],"proposes":[30],"a":[31,52,85,155,206],"strategy":[32],"for":[33,109,125,210],"interactions":[36,213],"using":[37],"non-intrusive":[38],"privacy-preserving":[40,208],"quality":[43],"sensors,":[44],"validated":[45],"through":[46],"an":[47],"empirical":[48],"conducted":[50],"in":[51,143,149,214],"controlled":[53],"climate":[54],"chamber.":[55],"We":[56,83],"deployed":[57],"27":[58],"sensors":[59],"monitor":[61],"11":[62],"categories":[63],"physical":[65],"indicators":[66,133],"systematically":[68],"varied":[69],"occupant":[70],"density,":[71],"activity":[72],"types,":[73],"air":[75],"change":[76],"rates":[77],"(ACH":[78],"0,":[79],"2,":[80],"4.39/h).":[82],"established":[84],"three-layer":[86],"feature":[87],"extraction":[88],"framework":[89],"comprising":[90],"universal":[91],"baseline,":[92],"indicator-specific,":[93],"features":[96],"compared":[98],"performance":[100,142,148],"linear":[102],"Logistic":[103],"Regression":[104],"non-linear":[106],"LightGBM":[107],"models":[108],"predicting":[110],"interactions.":[111],"Experimental":[112],"results":[113],"indicate":[114],"that":[115],"sound":[117],"level":[118],"serves":[119,204],"as":[120,135,205],"main":[122],"ambient":[123,202],"indicator":[124],"recognition,":[127],"near-perfect":[129],"classification":[130],"accuracy.":[131],"Environmental":[132],"such":[134],"CO2,":[136],"temperature,":[137],"TVOC":[139],"demonstrate":[140,180],"high":[141],"occupancy":[144],"detection,":[145],"but":[146],"reduced":[147],"interaction":[150],"detection.":[151],"Spatial":[152],"analysis":[153],"reveals":[154],"\u201cspatiotemporal":[156],"resolution":[157],"coupling\u201d":[158],"pattern:":[159],"high-frequency":[160],"monitoring":[161,169],"relies":[162],"heavily":[163],"on":[164,172],"near-field":[165],"sensing,":[166],"whereas":[167],"low-frequency":[168],"depends":[170],"predominantly":[171],"spatially":[173],"distributed":[174],"comparisons.":[175],"Furthermore,":[176],"multimodal":[177,200],"fusion":[178],"strategies":[179],"potential":[181],"mitigate":[183],"limitations":[185],"single":[187],"non-acoustic":[188],"indicators.":[189],"Ultimately,":[190],"by":[191],"aligning":[192],"sensor":[193],"layouts":[194],"with":[195],"frequency":[197],"leveraging":[199],"data,":[201],"powerful,":[207],"tool":[209],"capturing":[211],"buildings.":[215]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-06-20T00:00:00"}
