{"id":"W4205667984","doi":"10.1109/access.2021.3138051","title":"AI-Powered In-Vehicle Passenger Monitoring Using Low-Cost mm-Wave Radar","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Computer science; Artificial intelligence; Radar; Robustness (evolution); Random forest; Multiclass classification; Pattern recognition (psychology); Classifier (UML); Binary classification; k-nearest neighbors algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003049472,0.0005278509,0.0004509442,0.0005715719,0.0001769988,0.0004910794,0.0008047382,0.0004663337,0.001304199],"category_scores_gemma":[0.0007510777,0.0001927872,0.0002462369,0.0004031438,0.0001237851,0.0007418339,0.0003850563,0.0003999479,0.001070583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001976624,"about_ca_system_score_gemma":0.0002623656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008564069,"about_ca_topic_score_gemma":0.00116932,"domain_scores_codex":[0.9996436,0.00006971145,0.00001397765,0.0000633239,0.0001715912,0.00003788169],"domain_scores_gemma":[0.9996712,0.00008163233,0.00006748609,0.00004415248,0.0001147403,0.00002077922],"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":[0.0004395048,0.0002942583,0.01109054,0.000214674,0.00008541749,0.0001944292,0.0001465892,0.02087269,0.1975776,0.002673827,0.005702598,0.760708],"study_design_scores_gemma":[0.00006239185,0.0005135534,0.009279511,0.00003147731,0.00008375196,0.000841858,0.00006396763,0.8586732,0.1156196,0.001265405,0.0135113,0.00005404913],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03734284,0.0002693654,0.9574398,0.0001454799,0.00009311453,0.00006131506,0.0001084772,0.001830307,0.00270939],"genre_scores_gemma":[0.5978977,0.0003974575,0.3957309,0.0002870513,0.0001674793,0.0001291182,0.0003748517,0.00007709696,0.004938292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001304199,"threshold_uncertainty_score":0.004363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04932820708551423,"score_gpt":0.2986209056710053,"score_spread":0.2492926985854911,"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."}}