{"id":"W3163007459","doi":"10.1109/jbhi.2021.3082876","title":"Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Waterloo; University of Toronto; University Health Network","funders":"","keywords":"Computer science; Signal processing; Textile; Artificial intelligence; Residual; Noise (video); Coding (social sciences); SIGNAL (programming language); Pattern recognition (psychology); Algorithm; Digital signal processing; Mathematics; Computer hardware","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":[0.001999736,0.0006665686,0.0005951906,0.0004712925,0.0002201601,0.001112528,0.0008430254,0.001189005,0.001115414],"category_scores_gemma":[0.009786182,0.0002620998,0.0007016587,0.000542432,0.0006280063,0.00106414,0.0006260939,0.001139247,0.0006658155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004439343,"about_ca_system_score_gemma":0.0005931239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003408202,"about_ca_topic_score_gemma":0.001846602,"domain_scores_codex":[0.9994413,0.0002099355,0.00004277613,0.0001114012,0.0001545327,0.00004002481],"domain_scores_gemma":[0.9972367,0.001921065,0.0001561788,0.0003083859,0.0003199595,0.0000576627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001401228,0.00009017627,0.001470761,0.0001247691,0.00005698166,0.0001428757,0.00009264432,0.8635287,0.01213556,0.008950311,0.0008802575,0.1123869],"study_design_scores_gemma":[0.000004962465,0.00002514031,0.0002417922,0.000009312647,0.000004547234,0.00003907284,0.000008254562,0.9947721,0.001733219,0.002662945,0.0004926293,0.000005996125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01005358,0.0002150216,0.9885557,0.0001968292,0.0000335437,0.00003149733,0.00004256122,0.0002907117,0.0005806053],"genre_scores_gemma":[0.369647,0.001044189,0.6248408,0.000361665,0.0001177286,0.0001978217,0.0005244145,0.0003068685,0.002959507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003408202,"threshold_uncertainty_score":0.01057571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07373318436600074,"score_gpt":0.3771631000327959,"score_spread":0.3034299156667951,"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."}}