{"id":"W2083106564","doi":"10.1109/ccnc.2011.5766464","title":"Recognition of false alarms in fall detection systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"ALARM; Accelerometer; Wearable computer; Computer science; False alarm; Population; Poison control; Artificial intelligence; Computer security; Medical emergency; Medicine; Engineering; Embedded system","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":[0.003597503,0.001450997,0.001902684,0.002158276,0.0007966369,0.002034329,0.001804334,0.002478346,0.0006430669],"category_scores_gemma":[0.03736266,0.0007490855,0.0004498936,0.001079743,0.0006095544,0.002426751,0.001442619,0.00191292,0.000982819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000566051,"about_ca_system_score_gemma":0.0006533194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286557,"about_ca_topic_score_gemma":0.00109442,"domain_scores_codex":[0.9903487,0.002103683,0.001103285,0.001438782,0.004426229,0.0005793642],"domain_scores_gemma":[0.9642494,0.02172018,0.004463463,0.003130157,0.005798727,0.0006380879],"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":[0.002539324,0.000611604,0.06156707,0.0008765373,0.0004013727,0.004100159,0.00169661,0.04250131,0.09116708,0.00369473,0.01369475,0.7771496],"study_design_scores_gemma":[0.0001293055,0.001530491,0.04212752,0.0002629366,0.0004228346,0.009643804,0.0005111519,0.745833,0.1743912,0.01121801,0.01363548,0.0002942818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3608168,0.004438144,0.6169115,0.001418238,0.001086782,0.0002144341,0.0003702439,0.01159014,0.003153751],"genre_scores_gemma":[0.8966097,0.000541848,0.1001294,0.0006157898,0.0002181297,0.00008529943,0.0002804681,0.000180488,0.001338964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003597503,"threshold_uncertainty_score":0.01902562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07748503121588851,"score_gpt":0.2365775824812743,"score_spread":0.1590925512653858,"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."}}