{"id":"W2903765290","doi":"10.1109/antem.2018.8572982","title":"Remote Heart Rate Sensing with mm-wave Radar","year":2018,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Radar; Continuous-wave radar; Remote patient monitoring; Computer science; Wearable computer; Reliability (semiconductor); Remote sensing; Radar engineering details; Vital signs; Real-time computing; Electronic engineering; Radar imaging; Engineering; Telecommunications; Medicine; Embedded system; Geology; Physics","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":[0.0004252309,0.0003637074,0.0003632308,0.0002759589,0.00007825432,0.0003139309,0.0003716054,0.0005865669,0.001116207],"category_scores_gemma":[0.0008216693,0.0001317404,0.00021071,0.000219741,0.0001354048,0.0005019519,0.0004527814,0.0002756723,0.0005317331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007591873,"about_ca_system_score_gemma":0.00005782823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009707865,"about_ca_topic_score_gemma":0.00009051046,"domain_scores_codex":[0.9995913,0.0001393196,0.00001919487,0.0000760522,0.000147514,0.00002668546],"domain_scores_gemma":[0.9997153,0.0001211546,0.00004891758,0.00004056446,0.00005838331,0.00001569206],"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":[0.0003596694,0.0001451745,0.002961734,0.0003283132,0.00004108452,0.0002709593,0.0001583361,0.002178313,0.8808768,0.0006845318,0.0009355838,0.1110596],"study_design_scores_gemma":[0.0002502353,0.003245798,0.02849008,0.0001204353,0.0001709481,0.004139172,0.0002187492,0.116662,0.8297117,0.00135327,0.01551575,0.0001220686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5528728,0.006281158,0.4296435,0.0006786058,0.0004243602,0.0001523659,0.0001755015,0.001234414,0.008537338],"genre_scores_gemma":[0.9035511,0.001202096,0.09149557,0.0003394994,0.0001681389,0.00006260863,0.00009530158,0.00002798467,0.003057743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001116207,"threshold_uncertainty_score":0.003734112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611711450899815,"score_gpt":0.2113795231066795,"score_spread":0.1952624085976813,"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."}}