{"id":"W4405602138","doi":"10.1109/picom64201.2024.00025","title":"Multimodal Sequential Deep Learning for Agitation Detection in People Living with Dementia","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; University Health Network","funders":"","keywords":"Dementia; Assisted living; Computer science; Artificial intelligence; Deep learning; Medicine; Gerontology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004701091,0.0005292373,0.00031935,0.0004987029,0.000141189,0.0003379995,0.0002964434,0.0003529451,0.0009284653],"category_scores_gemma":[0.001907347,0.0001431262,0.0003502815,0.0002772538,0.0001250889,0.0004053747,0.0004686735,0.000577816,0.0002451992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000452569,"about_ca_system_score_gemma":0.0004051383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008112115,"about_ca_topic_score_gemma":0.01301574,"domain_scores_codex":[0.9998434,0.00004890612,0.00001235201,0.00003795375,0.00002460031,0.00003282957],"domain_scores_gemma":[0.9997548,0.0001052974,0.00003507367,0.00001628665,0.00005947395,0.0000290244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002696592,0.001028906,0.2067644,0.0003173323,0.0003482457,0.0009115984,0.0004521353,0.1275236,0.01281952,0.001356993,0.008949496,0.6368312],"study_design_scores_gemma":[0.00002778398,0.0003725273,0.03840576,0.00005119484,0.00007035225,0.0002182018,0.0001865919,0.9532295,0.003968552,0.002411113,0.001040495,0.00001805167],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.919014,0.001332249,0.07450838,0.0007691228,0.00009516633,0.00006952531,0.001229355,0.0006193575,0.002362912],"genre_scores_gemma":[0.9915066,0.0002028087,0.006917343,0.00007815417,0.00001814608,0.00002002068,0.0005077273,0.000006522926,0.0007424581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008112115,"threshold_uncertainty_score":0.01612979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758615457038338,"score_gpt":0.2581169417607062,"score_spread":0.2405307871903228,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}