{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002102739,0.0004501778,0.0005136732,0.0005620527,0.000122917,0.0003706503,0.0004045121,0.000457479,0.001612709],"category_scores_gemma":[0.0007620063,0.0001445399,0.0002509374,0.0003386645,0.00009364699,0.0005252213,0.0005131194,0.0003156105,0.001335038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001100164,"about_ca_system_score_gemma":0.0001382348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005866449,"about_ca_topic_score_gemma":0.0009043731,"domain_scores_codex":[0.9996318,0.00005412248,0.00002110225,0.0001254199,0.0001335676,0.00003401244],"domain_scores_gemma":[0.9997869,0.00005265713,0.00003718945,0.00002233874,0.00007877003,0.00002215416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000784967,0.0003599892,0.01805181,0.0006505477,0.0001250712,0.0004384267,0.0002750717,0.005992486,0.3624497,0.0004628907,0.00578063,0.6046284],"study_design_scores_gemma":[0.0001814044,0.002267076,0.2062671,0.0002773696,0.0003109427,0.003104592,0.0006850782,0.4354753,0.3250947,0.002091301,0.02404784,0.000197254],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5207003,0.001786154,0.4581428,0.0002702121,0.0003794858,0.0002525448,0.001495981,0.007554625,0.009417942],"genre_scores_gemma":[0.8958218,0.0006315272,0.09744179,0.0001871263,0.00009360077,0.0001618001,0.0009653813,0.00008873765,0.004608319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001612709,"threshold_uncertainty_score":0.005395114,"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."}}