{"id":"W3011543663","doi":"10.1109/sirf46766.2020.9040191","title":"On the Use of Low-Cost Radars and Machine Learning for In-Vehicle Passenger Monitoring","year":2020,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Support vector machine; Radar; Random forest; Capon; Computer science; Artificial intelligence; Covariance; Classifier (UML); Pattern recognition (psychology); Machine learning; Real-time computing; Telecommunications; Beamforming; Mathematics; Statistics","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":[0.0007454715,0.0004678531,0.0003710325,0.0006548223,0.0001599807,0.0005595472,0.0006534508,0.0006032668,0.001597532],"category_scores_gemma":[0.001619565,0.0001586294,0.0002192827,0.0005589667,0.0002568433,0.0009148456,0.0003207016,0.0004235523,0.0007537658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001852883,"about_ca_system_score_gemma":0.0002011418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006805362,"about_ca_topic_score_gemma":0.0008662826,"domain_scores_codex":[0.9993381,0.0002124879,0.00002317943,0.0001042106,0.0002790103,0.00004300132],"domain_scores_gemma":[0.9988855,0.0005715426,0.0001236943,0.0001059645,0.0002923092,0.00002100049],"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":[0.0002084958,0.0001620346,0.005590099,0.0003154801,0.00008873755,0.0001823649,0.00006029985,0.01502242,0.1145632,0.006651808,0.00209357,0.8550615],"study_design_scores_gemma":[0.00006259236,0.001701012,0.02080467,0.0001785328,0.0002271634,0.002364988,0.000147228,0.6691974,0.2416372,0.009342877,0.05421109,0.0001252324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04980689,0.004069011,0.9386722,0.0005917506,0.00016413,0.00005305971,0.00005669007,0.0007863206,0.005799875],"genre_scores_gemma":[0.622187,0.003651133,0.3666006,0.0005438879,0.0003331632,0.00005803413,0.0001818875,0.00006278638,0.006381662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001597532,"threshold_uncertainty_score":0.005344272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05692896912496497,"score_gpt":0.2284547919592536,"score_spread":0.1715258228342886,"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."}}