{"id":"W4372266511","doi":"10.1109/icassp49357.2023.10095831","title":"Unobtrusive Respiratory Monitoring System for Intensive Care","year":2023,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Crossover; Computer science; Optical flow; Feature (linguistics); Feature extraction; Intensive care; Point of care; Point (geometry); Breathing; Artificial intelligence; Real-time computing; Computer vision; Pattern recognition (psychology); Medicine; Intensive care medicine; Mathematics; Image (mathematics)","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":[0.0003676378,0.0008807443,0.000689221,0.0005694741,0.0002265303,0.0004567357,0.0009756428,0.0006785079,0.0042703],"category_scores_gemma":[0.001157762,0.0002256854,0.0002661689,0.0003296834,0.0001274026,0.0004898818,0.0007363528,0.000531982,0.001331993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000253481,"about_ca_system_score_gemma":0.000349875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008440317,"about_ca_topic_score_gemma":0.001628816,"domain_scores_codex":[0.9995448,0.00006961488,0.00003523795,0.0001586034,0.0001651767,0.00002659053],"domain_scores_gemma":[0.9995844,0.00009955827,0.00006613215,0.00006188155,0.000129434,0.00005866924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001339837,0.0005189932,0.01349923,0.0008203802,0.0001519258,0.0009890975,0.0002682676,0.007817749,0.2648902,0.0006599714,0.02563371,0.6834107],"study_design_scores_gemma":[0.000478702,0.00242584,0.09497979,0.0002814746,0.0002789831,0.004082059,0.0002918344,0.6135713,0.2318287,0.002346591,0.04919232,0.0002424201],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1606059,0.002945542,0.7994272,0.0006673857,0.0006630815,0.0008310272,0.003123644,0.02691764,0.004818579],"genre_scores_gemma":[0.6995953,0.001003767,0.2877209,0.0009268443,0.0003172012,0.0009020658,0.003769258,0.0003173283,0.005447333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0042703,"threshold_uncertainty_score":0.0142855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291005533318449,"score_gpt":0.2633709161206945,"score_spread":0.23046086078751,"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."}}