{"id":"https://openalex.org/W7124150162","doi":"https://doi.org/10.48550/arxiv.2601.08241","title":"Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence","display_name":"Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence","publication_year":2026,"publication_date":"2026-01-13","ids":{"openalex":"https://openalex.org/W7124150162","doi":"https://doi.org/10.48550/arxiv.2601.08241"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.08241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08241","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.08241","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040262258","display_name":"Michele Fiori","orcid":"https://orcid.org/0009-0000-2462-3075"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fiori, Michele","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068914794","display_name":"Gabriele Civitarese","orcid":"https://orcid.org/0000-0002-8247-2524"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Civitarese, Gabriele","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123022473","display_name":"Marco Colussi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Colussi, Marco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010533347","display_name":"Cl\u00e1udio Bettini","orcid":"https://orcid.org/0000-0002-1727-7650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bettini, Claudio","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040262258"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.2671000063419342,"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":0.2671000063419342,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.09920000284910202,"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/T12127","display_name":"Software System Performance and Reliability","score":0.06610000133514404,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/segmentation","display_name":"Segmentation","score":0.5791000127792358},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5666999816894531},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5407000184059143},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5182999968528748},{"id":"https://openalex.org/keywords/activities-of-daily-living","display_name":"Activities of daily living","score":0.4968000054359436},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.37790000438690186},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.33869999647140503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7533000111579895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6116999983787537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5819000005722046},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5791000127792358},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5666999816894531},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5407000184059143},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5182999968528748},{"id":"https://openalex.org/C79544238","wikidata":"https://www.wikidata.org/wiki/Q423243","display_name":"Activities of daily living","level":2,"score":0.4968000054359436},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C71611378","wikidata":"https://www.wikidata.org/wiki/Q5165191","display_name":"Contextual design","level":3,"score":0.32440000772476196},{"id":"https://openalex.org/C152223200","wikidata":"https://www.wikidata.org/wiki/Q3055471","display_name":"Smart environment","level":3,"score":0.31949999928474426},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.30730000138282776},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30000001192092896},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2662000060081482},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26030001044273376}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.08241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08241","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.08241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.08241","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unobtrusive":[0],"sensor-based":[1],"recognition":[2,84],"of":[3,5,41,66],"Activities":[4],"Daily":[6],"Living":[7],"(ADLs)":[8],"in":[9],"smart":[10],"homes":[11],"by":[12],"processing":[13],"data":[14],"collected":[15],"from":[16,133],"IoT":[17],"sensing":[18],"devices":[19],"supports":[20],"applications":[21],"such":[22],"as":[23],"healthcare,":[24],"safety,":[25],"and":[26,88,112],"energy":[27],"management.":[28],"Recent":[29],"zero-shot":[30,82],"methods":[31,72],"based":[32],"on":[33,45,54,108],"Large":[34],"Language":[35],"Models":[36],"(LLMs)":[37],"have":[38],"the":[39,43,62],"advantage":[40],"removing":[42],"reliance":[44],"labeled":[46],"ADL":[47,83],"sensor":[48],"data.":[49],"However,":[50],"existing":[51,69],"approaches":[52,70,107],"rely":[53],"time-based":[55,105],"segmentation,":[56],"which":[57],"is":[58],"poorly":[59],"aligned":[60],"with":[61,85,118],"contextual":[63],"reasoning":[64],"capabilities":[65],"LLMs.":[67],"Moreover,":[68],"lack":[71],"for":[73,92],"estimating":[74,93],"prediction":[75,94],"confidence.":[76,95],"This":[77],"paper":[78],"proposes":[79],"to":[80],"improve":[81],"event-based":[86,101],"segmentation":[87,102],"a":[89],"novel":[90],"method":[91],"Our":[96],"experimental":[97],"evaluation":[98],"shows":[99],"that":[100],"consistently":[103],"outperforms":[104],"LLM":[106],"complex,":[109],"realistic":[110],"datasets":[111],"surpasses":[113],"supervised":[114],"data-driven":[115],"methods,":[116],"even":[117],"relatively":[119],"small":[120],"LLMs":[121],"(e.g.,":[122],"Gemma":[123],"3":[124],"27B).":[125],"The":[126],"proposed":[127],"confidence":[128],"measure":[129],"effectively":[130],"distinguishes":[131],"correct":[132],"incorrect":[134],"predictions.":[135]},"counts_by_year":[],"updated_date":"2026-01-15T23:21:31.212559","created_date":"2026-01-15T00:00:00"}
