{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008661677,0.0001987402,0.0002061099,0.0002083191,0.0001085529,0.00004568651,0.0001733633,0.0001000559,0.000003158391],"category_scores_gemma":[0.0001289544,0.0002041957,0.00009980173,0.0004033868,0.00001775663,0.000145706,0.00006598746,0.0001217352,0.0002689928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003813343,"about_ca_system_score_gemma":0.00001969162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009945004,"about_ca_topic_score_gemma":0.000002707492,"domain_scores_codex":[0.998949,0.00001208174,0.0002205709,0.0002309124,0.0001648532,0.0004226002],"domain_scores_gemma":[0.9989093,0.0001783911,0.00002331411,0.0002566732,0.0005284816,0.0001038306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002872922,0.000003165342,0.02854059,0.002694029,0.0002380577,0.0001584495,0.004931584,0.01562847,0.9290004,0.001358437,0.006975119,0.010443],"study_design_scores_gemma":[0.0004909554,0.00006763099,0.001551133,0.0003354077,0.00002611439,0.000003694686,0.06215752,0.0003459537,0.930836,0.00004448303,0.003740628,0.0004004403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612764,0.001213425,0.00781027,0.00001959854,0.01003475,0.001048869,0.0000557415,0.006162029,0.01237894],"genre_scores_gemma":[0.9977895,0.000009652301,0.0005888279,0.00001455955,0.001134844,0.000241398,0.000007047476,0.00009854653,0.0001156283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05722593,"threshold_uncertainty_score":0.8326857,"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."}}