{"id":"W4293794926","doi":"10.1109/lmwc.2022.3198174","title":"Body Motion Artifact Cancellation Technique for Cough Detection Using FMCW Radar","year":2022,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Technology Letters","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Continuous-wave radar; Artifact (error); SIGNAL (programming language); Radar; Acoustics; Computer science; Bin; Vibration; Frequency modulation; Electronic engineering; Computer vision; Radar imaging; Physics; Radio frequency; Engineering; Telecommunications; Algorithm","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":[0.0003543431,0.0004309638,0.0003538344,0.0003892605,0.0001620032,0.0002442673,0.0004007243,0.000556258,0.0006061095],"category_scores_gemma":[0.000886113,0.0001539911,0.0002841336,0.0003116772,0.0001838999,0.0004512149,0.0002788537,0.0004319219,0.0003460078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001185458,"about_ca_system_score_gemma":0.0002126849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002851934,"about_ca_topic_score_gemma":0.0003693913,"domain_scores_codex":[0.9997003,0.00005498789,0.00001490631,0.00004123196,0.0001664306,0.00002216334],"domain_scores_gemma":[0.9996448,0.000137548,0.00005364231,0.00003083405,0.0001184246,0.00001483175],"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":[0.0002079403,0.00007738981,0.001231515,0.0002675462,0.00004330448,0.0001947356,0.00009472616,0.004627944,0.738673,0.001298297,0.001157913,0.2521258],"study_design_scores_gemma":[0.0000900443,0.001110053,0.00984329,0.00006293796,0.0001352122,0.002497889,0.00006523189,0.36958,0.6030526,0.0008805974,0.012592,0.00009001726],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04464285,0.001071088,0.952093,0.0001774426,0.0001320196,0.00006441371,0.00003751697,0.0005019273,0.001279725],"genre_scores_gemma":[0.5517209,0.001348819,0.443227,0.0004055208,0.0002006918,0.000134056,0.0001607115,0.00005863042,0.002743654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006061095,"threshold_uncertainty_score":0.00202769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009956496861435052,"score_gpt":0.2116665220973636,"score_spread":0.2017100252359286,"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."}}