{"id":"https://openalex.org/W3106682186","doi":"https://doi.org/10.1145/3408308.3427626","title":"Assessing ADL Routine Variability from High-dimensional Sensing Data using Hierarchical Clustering","display_name":"Assessing ADL Routine Variability from High-dimensional Sensing Data using Hierarchical Clustering","publication_year":2020,"publication_date":"2020-11-18","ids":{"openalex":"https://openalex.org/W3106682186","doi":"https://doi.org/10.1145/3408308.3427626","mag":"3106682186"},"language":"en","primary_location":{"id":"doi:10.1145/3408308.3427626","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3408308.3427626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","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/A5074670878","display_name":"Bogyeong Lee","orcid":"https://orcid.org/0000-0002-6620-7851"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bogyeong Lee","raw_affiliation_strings":["Texas A&M University, Texas US"],"affiliations":[{"raw_affiliation_string":"Texas A&M University, Texas US","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084121817","display_name":"Changbum R. Ahn","orcid":"https://orcid.org/0000-0002-6733-2216"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Changbum Ryan Ahn","raw_affiliation_strings":["Texas A&M University, Texas US"],"affiliations":[{"raw_affiliation_string":"Texas A&M University, Texas US","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109469674","display_name":"Prakhar Mohan","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]},{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prakhar Mohan","raw_affiliation_strings":["Amazon Web Services, Seattle US"],"affiliations":[{"raw_affiliation_string":"Amazon Web Services, Seattle US","institution_ids":["https://openalex.org/I1311688040","https://openalex.org/I58610484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029799530","display_name":"Theodora Chaspari","orcid":"https://orcid.org/0000-0002-7603-8633"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Theodora Chaspari","raw_affiliation_strings":["Texas A&M University, Texas US"],"affiliations":[{"raw_affiliation_string":"Texas A&M University, Texas US","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5074670878"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1954,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.51078847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"282","last_page":"285"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9990000128746033,"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":0.9990000128746033,"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/T10009","display_name":"Dementia and Cognitive Impairment Research","score":0.9460999965667725,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.9108999967575073,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/activities-of-daily-living","display_name":"Activities of daily living","score":0.7460200786590576},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7095288634300232},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7078403234481812},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6096655130386353},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.5911125540733337},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5109459757804871},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46191051602363586},{"id":"https://openalex.org/keywords/smart-environment","display_name":"Smart environment","score":0.4592258036136627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44723913073539734},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.41387176513671875},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3551899790763855},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10441580414772034},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.073414146900177},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06879475712776184}],"concepts":[{"id":"https://openalex.org/C79544238","wikidata":"https://www.wikidata.org/wiki/Q423243","display_name":"Activities of daily living","level":2,"score":0.7460200786590576},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7095288634300232},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7078403234481812},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6096655130386353},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.5911125540733337},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5109459757804871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46191051602363586},{"id":"https://openalex.org/C152223200","wikidata":"https://www.wikidata.org/wiki/Q3055471","display_name":"Smart environment","level":3,"score":0.4592258036136627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44723913073539734},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.41387176513671875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3551899790763855},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10441580414772034},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.073414146900177},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06879475712776184},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3408308.3427626","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3408308.3427626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1999478155","https://openalex.org/W2015778111","https://openalex.org/W2044465660","https://openalex.org/W2153506531","https://openalex.org/W2586341316","https://openalex.org/W2614272530","https://openalex.org/W2804865177","https://openalex.org/W3025030523","https://openalex.org/W3049524814","https://openalex.org/W4245302924"],"related_works":["https://openalex.org/W2071210425","https://openalex.org/W4255837520","https://openalex.org/W4200627986","https://openalex.org/W2103158077","https://openalex.org/W2208191412","https://openalex.org/W2413724037","https://openalex.org/W4310840813","https://openalex.org/W4232043044","https://openalex.org/W4248940146","https://openalex.org/W3200375535"],"abstract_inverted_index":{"Irregular":[0],"patterns":[1],"of":[2,4,15,21,39,121],"Activities":[3],"Daily":[5],"Living":[6],"(ADLs)":[7],"are":[8],"associated":[9],"with":[10],"mild":[11],"cognitive":[12],"impairment":[13],"(MCI)":[14],"older":[16],"adults.":[17],"Measuring":[18],"the":[19,101,114,119],"variability":[20,120],"ADL":[22,88,122],"routines":[23,123],"using":[24,124],"various":[25],"non-intrusive":[26,126],"sensors":[27],"in":[28,61,103],"smart":[29],"home":[30,73],"environments":[31],"presents":[32],"a":[33],"great":[34],"opportunity":[35],"for":[36,67,71],"early":[37],"diagnosis":[38],"MCI.":[40],"However,":[41],"existing":[42],"studies":[43],"mostly":[44],"rely":[45],"on":[46,99],"supervised":[47],"learning":[48],"approaches":[49],"to":[50,86],"recognize":[51],"ADLs":[52],"and":[53,64,90],"measure":[54,91],"their":[55,92],"variabilities,":[56],"which":[57],"requires":[58],"large":[59],"efforts":[60],"human":[62],"observation":[63],"manual":[65],"annotation":[66],"constructing":[68],"training":[69],"datasets":[70],"each":[72],"environment.":[74],"In":[75,94],"this":[76,78,96],"context,":[77],"study":[79,97],"proposes":[80],"an":[81],"unsupervised":[82],"hierarchical":[83],"clustering":[84],"method":[85,116],"capture":[87,118],"clusters":[89],"variabilities.":[93],"particular,":[95],"focuses":[98],"addressing":[100],"challenge":[102],"employing":[104],"data":[105],"from":[106],"multiple":[107],"heterogenous":[108],"sensors.":[109],"The":[110],"results":[111],"show":[112],"that":[113],"proposed":[115],"can":[117],"high-dimensional":[125],"sensing":[127],"data.":[128]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}