{"id":"W4405902202","doi":"10.48550/arxiv.2412.19254","title":"Leveraging Self-Training and Variational Autoencoder for Agitation Detection in People with Dementia Using Wearable Sensors","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Wearable computer; Training (meteorology); Dementia; Computer science; Artificial intelligence; Physical medicine and rehabilitation; Training set; Psychology; Machine learning; Human–computer interaction; Pattern recognition (psychology); Medicine; Embedded system; Deep learning; Geography; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006487308,0.0006248933,0.0006188142,0.0004035763,0.0002163503,0.0003843723,0.0005928939,0.0005921795,0.0003621253],"category_scores_gemma":[0.001480615,0.0002894797,0.0006824333,0.0002787292,0.0002641124,0.000474702,0.0004777247,0.0008089561,0.0001870014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002975071,"about_ca_system_score_gemma":0.0004245674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006676691,"about_ca_topic_score_gemma":0.006688234,"domain_scores_codex":[0.9997562,0.00005886691,0.00001970736,0.00008022883,0.00004592393,0.00003915171],"domain_scores_gemma":[0.999684,0.000154776,0.00003311398,0.00002678339,0.00008459475,0.00001671379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004530431,0.0005095683,0.02094139,0.0001696384,0.000313318,0.0003452636,0.0002640799,0.4698781,0.03174836,0.001450911,0.002681063,0.4712452],"study_design_scores_gemma":[0.000004075797,0.00004196453,0.002245615,0.000008669291,0.00001384282,0.00003189754,0.00001413597,0.9946244,0.002412557,0.0003604773,0.0002353558,0.000006975889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2646226,0.001293451,0.7303018,0.0003117905,0.0001339163,0.00008065829,0.0001632915,0.001025856,0.002066561],"genre_scores_gemma":[0.9132551,0.0003881657,0.08361629,0.0001601,0.00005171788,0.00006121003,0.0004086914,0.00003432393,0.002024336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006676691,"threshold_uncertainty_score":0.01327568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08374787501556262,"score_gpt":0.2049069851148038,"score_spread":0.1211591100992412,"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."}}