{"id":"https://openalex.org/W4385847493","doi":"https://doi.org/10.1145/3582515.3609551","title":"Leveraging Self-Supervised Learning for Human Activity Recognition with Ambient Sensors","display_name":"Leveraging Self-Supervised Learning for Human Activity Recognition with Ambient Sensors","publication_year":2023,"publication_date":"2023-08-15","ids":{"openalex":"https://openalex.org/W4385847493","doi":"https://doi.org/10.1145/3582515.3609551"},"language":"en","primary_location":{"id":"doi:10.1145/3582515.3609551","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3582515.3609551","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3582515.3609551","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Conference on Information Technology for Social Good","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/10.1145/3582515.3609551","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001631809","display_name":"Hui Chen","orcid":"https://orcid.org/0000-0003-4401-268X"},"institutions":[{"id":"https://openalex.org/I135117807","display_name":"Universit\u00e9 de Sherbrooke","ror":"https://ror.org/00kybxq39","country_code":"CA","type":"education","lineage":["https://openalex.org/I135117807"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Hui Chen","raw_affiliation_strings":["Computer Science, Universit\u00e9 de Sherbrooke, Canada"],"affiliations":[{"raw_affiliation_string":"Computer Science, Universit\u00e9 de Sherbrooke, Canada","institution_ids":["https://openalex.org/I135117807"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101743786","display_name":"Charles Gouin-Vallerand","orcid":"https://orcid.org/0000-0002-5811-2932"},"institutions":[{"id":"https://openalex.org/I135117807","display_name":"Universit\u00e9 de Sherbrooke","ror":"https://ror.org/00kybxq39","country_code":"CA","type":"education","lineage":["https://openalex.org/I135117807"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Charles Gouin-Vallerand","raw_affiliation_strings":["Universit\u00e9 de Sherbrooke, Canada"],"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Sherbrooke, Canada","institution_ids":["https://openalex.org/I135117807"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009711297","display_name":"K\u00e9vin Bouchard","orcid":"https://orcid.org/0000-0002-5227-6602"},"institutions":[{"id":"https://openalex.org/I104914703","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi","ror":"https://ror.org/00y3hzd62","country_code":"CA","type":"education","lineage":["https://openalex.org/I104914703","https://openalex.org/I49663120"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"K\u00e9vin Bouchard","raw_affiliation_strings":["Informatique et math\u00e9matique, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, Canada"],"affiliations":[{"raw_affiliation_string":"Informatique et math\u00e9matique, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, Canada","institution_ids":["https://openalex.org/I104914703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035113421","display_name":"S\u00e9bastien Gaboury","orcid":"https://orcid.org/0000-0001-7749-3470"},"institutions":[{"id":"https://openalex.org/I104914703","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi","ror":"https://ror.org/00y3hzd62","country_code":"CA","type":"education","lineage":["https://openalex.org/I104914703","https://openalex.org/I49663120"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"S\u00e9bastien Gaboury","raw_affiliation_strings":["informatique et math\u00e9matique, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, Canada"],"affiliations":[{"raw_affiliation_string":"informatique et math\u00e9matique, Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, Canada","institution_ids":["https://openalex.org/I104914703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030377004","display_name":"M\u00e9lanie Couture","orcid":"https://orcid.org/0000-0002-0088-3865"},"institutions":[{"id":"https://openalex.org/I135117807","display_name":"Universit\u00e9 de Sherbrooke","ror":"https://ror.org/00kybxq39","country_code":"CA","type":"education","lineage":["https://openalex.org/I135117807"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M\u00e9lanie Couture","raw_affiliation_strings":["Universit\u00e9 de Sherbrooke, Canada"],"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Sherbrooke, Canada","institution_ids":["https://openalex.org/I135117807"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062674788","display_name":"Nathalie Bier","orcid":"https://orcid.org/0000-0002-2940-694X"},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nathalie Bier","raw_affiliation_strings":["Universit\u00e9 de Montr\u00e9al, Canada"],"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086599189","display_name":"Sylvain Giroux","orcid":"https://orcid.org/0000-0003-0602-5957"},"institutions":[{"id":"https://openalex.org/I135117807","display_name":"Universit\u00e9 de Sherbrooke","ror":"https://ror.org/00kybxq39","country_code":"CA","type":"education","lineage":["https://openalex.org/I135117807"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sylvain Giroux","raw_affiliation_strings":["Universit\u00e9 de Sherbrooke, Canada"],"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Sherbrooke, Canada","institution_ids":["https://openalex.org/I135117807"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5001631809"],"corresponding_institution_ids":["https://openalex.org/I135117807"],"apc_list":null,"apc_paid":null,"fwci":1.0832,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.796043,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"324","last_page":"332"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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/T12222","display_name":"IoT-based Smart Home Systems","score":0.9606000185012817,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.951200008392334,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.8433270454406738},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.794947624206543},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7348402142524719},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6465031504631042},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5630589127540588},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5274964570999146},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.505341112613678},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5023660659790039},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4745769500732422},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.47120875120162964},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.470413476228714},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35090821981430054},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20645466446876526},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14465928077697754}],"concepts":[{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.8433270454406738},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.794947624206543},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7348402142524719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6465031504631042},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5630589127540588},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5274964570999146},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.505341112613678},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5023660659790039},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4745769500732422},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.47120875120162964},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.470413476228714},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35090821981430054},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20645466446876526},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14465928077697754},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3582515.3609551","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3582515.3609551","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3582515.3609551","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Conference on Information Technology for Social Good","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3582515.3609551","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3582515.3609551","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3582515.3609551","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Conference on Information Technology for Social Good","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320312535","display_name":"AGE-WELL","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385847493.pdf","grobid_xml":"https://content.openalex.org/works/W4385847493.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2015200434","https://openalex.org/W2047286045","https://openalex.org/W2103388129","https://openalex.org/W2344636181","https://openalex.org/W2794717185","https://openalex.org/W2940791683","https://openalex.org/W2951929328","https://openalex.org/W3099025572","https://openalex.org/W3099616155","https://openalex.org/W3114632476","https://openalex.org/W3164845984","https://openalex.org/W3171007011","https://openalex.org/W3182236157","https://openalex.org/W3184978184","https://openalex.org/W3195262491","https://openalex.org/W3206924601","https://openalex.org/W4214542585","https://openalex.org/W4226247552","https://openalex.org/W4286986228","https://openalex.org/W4318148707"],"related_works":["https://openalex.org/W3192840557","https://openalex.org/W3091976719","https://openalex.org/W3018421652","https://openalex.org/W4220996320","https://openalex.org/W4299822940","https://openalex.org/W2279398222","https://openalex.org/W4312200629","https://openalex.org/W4285149559","https://openalex.org/W4382286161","https://openalex.org/W3021430260"],"abstract_inverted_index":{"Human":[0],"activity":[1,70],"recognition":[2,71],"(HAR)":[3],"using":[4,72],"ambient":[5,73],"sensors":[6],"has":[7],"emerged":[8],"as":[9],"a":[10,58,94],"promising":[11],"approach":[12],"to":[13,105],"telemonitoring":[14],"daily":[15,69],"activities":[16],"and":[17,50,109,136,149],"enhancing":[18],"the":[19,63,80,83,103,113,117,140,144,159],"elderly":[20],"quality":[21],"of":[22,40,79,88,119,143,161],"life.":[23],"Deep":[24],"learning":[25,65,148,151,163],"models":[26],"have":[27],"demonstrated":[28],"competitive":[29],"performance":[30,142],"in":[31,112,146,164],"HAR":[32,178],"on":[33,62,128],"real-world":[34,177],"datasets.":[35],"However,":[36],"acquiring":[37],"large":[38],"amounts":[39],"annotated":[41],"sensor":[42,74,114,170],"data":[43],"for":[44,68,122,176],"extracting":[45,165],"robust":[46],"features":[47,121],"is":[48,82],"costly":[49,173],"time-consuming.":[51],"To":[52],"overcome":[53],"this":[54],"limitation,":[55],"we":[56,138],"propose":[57],"novel":[59],"model":[60,81,104,145],"based":[61],"self-supervised":[64,162],"framework,":[66],"SimCLR,":[67],"data.":[75],"The":[76,156],"core":[77],"component":[78],"encoder":[84],"module,":[85],"which":[86],"consists":[87],"two":[89],"convolutional":[90],"layers":[91],"followed":[92],"by":[93],"long":[95],"short-term":[96],"memory":[97],"(LSTM)":[98],"layer.":[99],"This":[100],"architecture":[101],"allows":[102],"capture":[106],"both":[107],"spatial":[108],"temporal":[110],"dependencies":[111],"data,":[115,171],"enabling":[116],"extraction":[118],"informative":[120],"downstream":[123],"tasks.":[124],"Through":[125],"extensive":[126],"experiments":[127],"three":[129],"CASAS":[130],"smart":[131],"home":[132],"datasets":[133],"(Aruba-1,":[134],"Aruba-2,":[135],"Milan),":[137],"showcase":[139],"superior":[141],"semi-supervised":[147],"transfer":[150],"scenarios,":[152],"surpassing":[153],"state-of-the-art":[154],"approaches.":[155],"findings":[157],"highlight":[158],"potential":[160],"valuable":[166],"information":[167],"from":[168],"unlabeled":[169],"reducing":[172],"annotation":[174],"efforts":[175],"applications.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}