{"id":"W2997667424","doi":"10.1109/jbhi.2019.2963388","title":"Highly Accurate Bathroom Activity Recognition Using Infrared Proximity Sensors","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Toilet; Computer science; Reliability (semiconductor); Limiting; Activity recognition; Assisted living; Artificial intelligence; Human–computer interaction; Computer vision; Medicine; Engineering; Gerontology; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002133473,0.0001555614,0.0004910612,0.0003278315,0.000143981,0.0001638777,0.0002888739,0.0001423355,0.00001541421],"category_scores_gemma":[0.00008212266,0.0001230541,0.00008746233,0.0003862673,0.00007380069,0.002702901,0.00008590712,0.0004677571,0.00004272609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001679673,"about_ca_system_score_gemma":0.0006711442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000387658,"about_ca_topic_score_gemma":0.000003578083,"domain_scores_codex":[0.9974905,0.0001544224,0.001249302,0.0001052008,0.0006735377,0.0003270597],"domain_scores_gemma":[0.9971223,0.0002004766,0.001689662,0.0002003613,0.0003292808,0.0004578798],"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":[0.0001061328,0.0003500974,0.0009170762,0.001584371,0.0001261439,0.00002236286,0.007215231,0.0000311872,0.0009759515,0.00005328357,0.00307912,0.985539],"study_design_scores_gemma":[0.01026,0.005399091,0.01511422,0.003679527,0.0000806617,0.006620087,0.002307783,0.8971195,0.004352023,0.004739277,0.04880714,0.001520756],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7444289,0.00004573684,0.2515528,0.00205618,0.001475835,0.0002700006,0.00001491652,0.0000328909,0.0001227974],"genre_scores_gemma":[0.9740591,0.0001543877,0.02432169,0.001150797,0.0002774935,0.000001625661,0.000002339389,0.000007521518,0.00002505279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9840183,"threshold_uncertainty_score":0.5017999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08199362234265468,"score_gpt":0.3231938322675901,"score_spread":0.2412002099249355,"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."}}