{"id":"W2903297675","doi":"10.1155/2018/8513487","title":"Video Analytic Based Health Monitoring for Driver in Moving Vehicle by Extracting Effective Heart Rate Inducing Features","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation of Korea; Fonds National de la Recherche Luxembourg; Army Research Laboratory; Korea University; National Research Foundation","keywords":"Hilbert–Huang transform; Computer science; Artificial intelligence; Computer vision; Face (sociological concept); SIGNAL (programming language); Analytics; Key (lock); Pattern recognition (psychology); Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001680586,0.0004565223,0.0003033335,0.000998208,0.00009582752,0.0003376277,0.0003136613,0.0002759697,0.0006029115],"category_scores_gemma":[0.0006007265,0.0001084425,0.0002186612,0.0003953872,0.00009464096,0.000291993,0.0002237944,0.0002467094,0.0002796542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001100097,"about_ca_system_score_gemma":0.0002020818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454426,"about_ca_topic_score_gemma":0.002090936,"domain_scores_codex":[0.9998766,0.00001600609,0.000007458376,0.00003989032,0.00004431579,0.00001559859],"domain_scores_gemma":[0.9998341,0.00003737332,0.00003378943,0.00001317769,0.00006784054,0.0000137747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005017113,0.0002461653,0.01775787,0.0003877148,0.000099783,0.0004284745,0.0002401425,0.02081589,0.2401134,0.001242876,0.003674535,0.7144915],"study_design_scores_gemma":[0.00004659805,0.0005923905,0.08707014,0.00004904305,0.0001637839,0.001133876,0.0003426783,0.8011632,0.1019925,0.001647455,0.005732188,0.00006615703],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2812894,0.001042435,0.7123991,0.0002148704,0.0001101578,0.000160801,0.0008729697,0.0012318,0.002678596],"genre_scores_gemma":[0.8394865,0.0008210276,0.15704,0.00008023783,0.0001320934,0.0001083465,0.0008902847,0.00004808578,0.001393416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001454426,"threshold_uncertainty_score":0.002891898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00863380115961925,"score_gpt":0.2729126479607102,"score_spread":0.264278846801091,"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."}}