{"id":"W2075023189","doi":"10.1002/sec.227","title":"Medical biometrics in mobile health monitoring","year":2010,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Computer science; Biometrics; Anonymity; Dependency (UML); Field (mathematics); Computer security; Protocol (science); Mobile device; Matching (statistics); Feature (linguistics); Human–computer interaction; Artificial intelligence; World Wide Web","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":[0.00113152,0.0003425185,0.0004416449,0.0007492401,0.0002913545,0.001177019,0.0004332448,0.00112648,0.002462506],"category_scores_gemma":[0.002422519,0.0001702939,0.0001883607,0.000822629,0.0005425016,0.0009152789,0.000780159,0.000542119,0.001230734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000269445,"about_ca_system_score_gemma":0.0001647324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003208922,"about_ca_topic_score_gemma":0.0002187139,"domain_scores_codex":[0.9986926,0.0006359919,0.00005842892,0.0001972602,0.0003613967,0.00005424217],"domain_scores_gemma":[0.9988747,0.0005439472,0.00017084,0.0001966436,0.0001716609,0.00004211911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009424693,0.0002154543,0.01486166,0.0005404827,0.0001077948,0.001299136,0.0004544762,0.02562711,0.1402719,0.0368176,0.006796168,0.7720658],"study_design_scores_gemma":[0.0001340021,0.001757744,0.04414453,0.0005852443,0.0002327129,0.01089678,0.000629887,0.597529,0.1615358,0.0537143,0.1285954,0.0002445706],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1575343,0.01541829,0.7998234,0.002528686,0.00076988,0.0002167202,0.00032216,0.00131989,0.02206662],"genre_scores_gemma":[0.8693888,0.003527691,0.1195189,0.0003648744,0.0003539953,0.0000842899,0.0001467129,0.00002541499,0.006589233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002462506,"threshold_uncertainty_score":0.008237898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301593942982866,"score_gpt":0.3252189151725061,"score_spread":0.3122029757426775,"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."}}