{"id":"https://openalex.org/W7138999465","doi":"https://doi.org/10.48550/arxiv.2603.17148","title":"Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning","display_name":"Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7138999465","doi":"https://doi.org/10.48550/arxiv.2603.17148"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.17148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17148","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.17148","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093761533","display_name":"Awatif Yasmin","orcid":"https://orcid.org/0009-0009-0599-6568"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yasmin, Awatif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043555436","display_name":"Tarek Mahmud","orcid":"https://orcid.org/0000-0002-1238-9397"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahmud, Tarek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068748275","display_name":"Sana Alamgeer","orcid":"https://orcid.org/0000-0002-6472-7570"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alamgeer, Sana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073060952","display_name":"Anne HH. Ngu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ngu, Anne H. H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.8517000079154968,"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":0.8517000079154968,"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/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.08299999684095383,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.023800000548362732,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.6478999853134155},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.6186000108718872},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.4781999886035919},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4603999853134155},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.40119999647140503},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.36660000681877136},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3425999879837036}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7610999941825867},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.6478999853134155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6194999814033508},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.6186000108718872},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5853000283241272},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.4781999886035919},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4603999853134155},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.40119999647140503},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C2777598771","wikidata":"https://www.wikidata.org/wiki/Q5341279","display_name":"Educational data mining","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C151913843","wikidata":"https://www.wikidata.org/wiki/Q3454555","display_name":"Dominance (genetics)","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2775999903678894},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.17148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17148","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.17148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17148","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"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":{"Personalized":[0],"fall":[1,25,48],"detection":[2],"models":[3],"can":[4],"significantly":[5],"improve":[6],"accuracy":[7],"by":[8,20],"adapting":[9],"to":[10,46,66,96,118],"individual":[11],"motion":[12],"patterns,":[13],"yet":[14],"their":[15],"effectiveness":[16,137],"is":[17,78],"often":[18],"limited":[19],"the":[21,28,37,70,109,113,123,128,136],"scarcity":[22],"of":[23,30,138],"real-world":[24,142],"data":[26],"and":[27,42,68,92],"dominance":[29],"non-fall":[31],"feedback":[32,74],"samples.":[33,75],"This":[34],"imbalance":[35],"biases":[36],"model":[38],"toward":[39],"routine":[40],"activities":[41],"weakens":[43],"its":[44],"sensitivity":[45],"true":[47],"events.":[49],"To":[50],"address":[51],"this":[52],"challenge,":[53],"we":[54],"propose":[55],"a":[56,119,132],"personalization":[57,140],"framework":[58,77],"that":[59,108],"combines":[60],"semi-supervised":[61],"clustering":[62],"with":[63,104,116,131],"contrastive":[64],"learning":[65,100],"identify":[67],"balance":[69],"most":[71],"informative":[72],"user":[73],"The":[76],"evaluated":[79],"under":[80],"three":[81],"retraining":[82],"strategies,":[83],"including":[84],"Training":[85],"from":[86],"Scratch":[87],"(TFS),":[88],"Transfer":[89],"Learning":[90,94],"(TL),":[91],"Few-Shot":[93],"(FSL),":[95],"assess":[97],"adaptability":[98],"across":[99],"paradigms.":[101],"Real-time":[102],"experiments":[103],"ten":[105],"participants":[106],"show":[107],"TFS":[110],"approach":[111],"achieves":[112,127],"highest":[114],"performance,":[115],"up":[117],"25%":[120],"improvement":[121],"over":[122],"baseline,":[124],"while":[125],"FSL":[126],"second-highest":[129],"performance":[130],"7%":[133],"improvement,":[134],"demonstrating":[135],"selective":[139],"for":[141],"deployment.":[143]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-20T00:00:00"}
