{"id":"W2600298925","doi":"10.1109/access.2017.2678521","title":"Heart Rate Variability Extraction From Videos Signals: ICA vs. EVM Comparison","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Photoplethysmogram; Heart rate variability; Independent component analysis; Computer science; Artificial intelligence; SIGNAL (programming language); Blind signal separation; Pattern recognition (psychology); Magnification; Face (sociological concept); Computer vision; Speech recognition; Heart rate; Channel (broadcasting); Medicine; Telecommunications; Blood pressure","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":[0.002086716,0.001531018,0.0009787248,0.003382584,0.0002608382,0.001135886,0.000501921,0.001335718,0.001570731],"category_scores_gemma":[0.007088202,0.0001915845,0.001173227,0.001598265,0.0003276549,0.001150519,0.0006670325,0.0007946528,0.0007800285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002167347,"about_ca_system_score_gemma":0.0004334346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001774771,"about_ca_topic_score_gemma":0.001436664,"domain_scores_codex":[0.9989907,0.000277254,0.0001017509,0.0002085605,0.0003140822,0.0001076807],"domain_scores_gemma":[0.9980549,0.001090293,0.0001230882,0.0001756471,0.0005007188,0.00005531132],"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":[0.001526844,0.0002581565,0.006442746,0.0005984132,0.0005413648,0.000232562,0.0001486607,0.03921833,0.03836141,0.001598522,0.003819663,0.9072534],"study_design_scores_gemma":[0.0001831263,0.001112038,0.07664913,0.0001690543,0.0007447453,0.001371068,0.0003781514,0.8413256,0.06655943,0.002944052,0.008375948,0.0001876103],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2297447,0.008939665,0.7465895,0.0006420637,0.001170576,0.0003158924,0.001502675,0.004282764,0.006812185],"genre_scores_gemma":[0.6551906,0.005014072,0.3317737,0.0002238873,0.0007166667,0.0003438559,0.00382166,0.0003883673,0.002527319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003382584,"threshold_uncertainty_score":0.01103574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05198566846954471,"score_gpt":0.3452192577589332,"score_spread":0.2932335892893885,"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."}}