{"id":"W3040629071","doi":"10.1109/tifs.2020.3006313","title":"Evaluation of the Time Stability and Uniqueness in PPG-Based Biometric System","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Bank of Canada","keywords":"Computer science; Biometrics; Software portability; Convolutional neural network; Robustness (evolution); Deep learning; Dynamic time warping; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Real-time computing; Data mining; 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":[0.001305257,0.0004762048,0.000660462,0.0007180387,0.0002436946,0.0006264703,0.0004410328,0.0009176806,0.001288539],"category_scores_gemma":[0.005184494,0.00008831699,0.0002613271,0.0003652171,0.0002352219,0.0008310256,0.0008088016,0.0003354831,0.0005844891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002640205,"about_ca_system_score_gemma":0.0002603981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000728505,"about_ca_topic_score_gemma":0.0007756264,"domain_scores_codex":[0.9987544,0.000228868,0.00009057175,0.00031564,0.0005049288,0.0001055482],"domain_scores_gemma":[0.9987205,0.0004619802,0.000197227,0.0001765485,0.0003723909,0.00007124712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005003916,0.0005202427,0.07896314,0.000874201,0.000471396,0.001429864,0.0003158387,0.04322659,0.2792821,0.002047214,0.00585578,0.5820099],"study_design_scores_gemma":[0.00007443147,0.002248619,0.1571155,0.00009318897,0.0002421704,0.004546856,0.0003551923,0.6341255,0.1952944,0.001362997,0.004421904,0.0001192257],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.874843,0.001800418,0.1169394,0.0002315422,0.0002806238,0.0001058572,0.001185322,0.00102777,0.003586145],"genre_scores_gemma":[0.9831109,0.0002685803,0.01469743,0.00005900754,0.0000550879,0.00003482089,0.0007673561,0.00003064011,0.0009760808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001305257,"threshold_uncertainty_score":0.006902933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957476067454317,"score_gpt":0.2112677925495113,"score_spread":0.1916930318749682,"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."}}