{"id":"W3091660797","doi":"10.1364/isa.2020.iw3d.3","title":"Motion-Tolerant Remote Respiration Monitoring with a Multi-Camera Configuration","year":2020,"lang":"en","type":"article","venue":"Imaging and Applied Optics Congress","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Waveform; Computer science; Computer vision; Respiratory monitoring; Motion (physics); Artificial intelligence; Respiration; Respiratory rate; Isolation (microbiology); Motion estimation; Real-time computing; Respiratory system; Telecommunications; Heart rate; Medicine","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":[0.00006839771,0.0002096631,0.000181901,0.00005408962,0.0001243776,0.0002100637,0.00008497028,0.00004331175,0.000002272435],"category_scores_gemma":[0.00001697389,0.0002085669,0.00001897165,0.0001519314,0.00005799171,0.0002002058,0.00002461207,0.0002155308,0.00001348565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003381139,"about_ca_system_score_gemma":0.00001058489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008604648,"about_ca_topic_score_gemma":9.654448e-7,"domain_scores_codex":[0.9990987,0.00001101133,0.0002098039,0.000268488,0.0001763132,0.0002356827],"domain_scores_gemma":[0.9995579,0.00004264426,0.00004940875,0.0001367298,0.00007198572,0.0001414071],"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":[0.00005525967,0.00001660817,0.006429318,0.0001860291,0.00006573791,0.00004126185,0.001398836,0.0798598,0.8382275,0.0004462909,0.00003016133,0.07324316],"study_design_scores_gemma":[0.00194812,0.00004280153,0.001801462,0.0002194072,0.00005888508,0.00001548966,0.0007565716,0.3831468,0.6109479,0.00005956685,0.0003608777,0.0006420899],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.510088,0.0003855401,0.4857532,0.0002738337,0.0005358261,0.0004083226,0.000007191161,0.0006231982,0.001924869],"genre_scores_gemma":[0.9803356,0.00003600354,0.01909636,0.00005073017,0.0003971354,0.00001443661,0.000007203135,0.00005340112,0.00000914723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4702476,"threshold_uncertainty_score":0.8505113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925865654546179,"score_gpt":0.2284510761102433,"score_spread":0.2091924195647815,"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."}}