{"id":"W4206758809","doi":"10.1109/bibm52615.2021.9669735","title":"Heart Rate Monitoring Using PPG With Smartphone Camera","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Photoplethysmogram; Artificial intelligence; Computer vision; Computer science; SIGNAL (programming language); Pixel; Convolutional neural network; Noise (video); Filter (signal processing)","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":[0.0001651676,0.0004897607,0.0004924231,0.000663951,0.0001039084,0.0003504013,0.0002466537,0.000419817,0.002909021],"category_scores_gemma":[0.0006573864,0.000126262,0.0001943287,0.0004437949,0.0000645275,0.0002957055,0.0002686349,0.0001940022,0.001025987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055105,"about_ca_system_score_gemma":0.0001284926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001113472,"about_ca_topic_score_gemma":0.001488262,"domain_scores_codex":[0.9996504,0.00005416934,0.0000210479,0.00009929914,0.0001549908,0.0000200392],"domain_scores_gemma":[0.999721,0.00005447624,0.00003846236,0.00003064484,0.0001386496,0.00001675843],"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":[0.0008980201,0.0001878508,0.02456968,0.001233261,0.0001755035,0.0009195541,0.0001802974,0.003604339,0.4294358,0.0005443757,0.01049956,0.5277517],"study_design_scores_gemma":[0.00035345,0.003084372,0.3793057,0.0003207417,0.0004487663,0.008599832,0.0002415398,0.2418112,0.3330512,0.001065665,0.03147175,0.0002457988],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4868025,0.00469758,0.4684525,0.0004381949,0.000575713,0.0007602724,0.006173611,0.01052549,0.02157405],"genre_scores_gemma":[0.8917921,0.002050906,0.09531277,0.0003550348,0.0002783151,0.0003457325,0.001697128,0.0001245693,0.008043361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002909021,"threshold_uncertainty_score":0.00973171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04810541512609177,"score_gpt":0.2791273793029019,"score_spread":0.2310219641768101,"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."}}