{"id":"W4402626732","doi":"10.1109/access.2024.3463742","title":"Optimization of Wearable Biosensor Data for Stress Classification Using Machine Learning and Explainable AI","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Wearable computer; Machine learning; Artificial intelligence; Biosensor; Stress (linguistics); Pattern recognition (psychology); Embedded system; Nanotechnology; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009185413,0.000861613,0.0007377087,0.0006094885,0.0002323656,0.000557258,0.0005361062,0.0004931265,0.0006271683],"category_scores_gemma":[0.002354117,0.0002842926,0.0007951591,0.0005189577,0.0002083989,0.0004534934,0.0003645624,0.0005911029,0.000223944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003415156,"about_ca_system_score_gemma":0.0004638121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002282517,"about_ca_topic_score_gemma":0.001949142,"domain_scores_codex":[0.9997111,0.00009782071,0.0000197622,0.00006346057,0.00007486418,0.00003298524],"domain_scores_gemma":[0.9995683,0.0002185803,0.00007182131,0.00004237648,0.00008645873,0.00001238149],"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":[0.0002444054,0.000361827,0.008431363,0.0001079323,0.0001688548,0.00009336598,0.0000828922,0.7392849,0.02402511,0.001777007,0.0005710509,0.2248511],"study_design_scores_gemma":[0.000006022087,0.00006273137,0.002413191,0.000003636578,0.000008804705,0.00001036676,0.000008520379,0.9945809,0.002163184,0.0005874183,0.0001499907,0.000005135827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1419249,0.0003305798,0.8556939,0.0001925511,0.00003492572,0.00008247071,0.0001032699,0.0006639147,0.0009736139],"genre_scores_gemma":[0.8391651,0.0002064646,0.1591386,0.00007059499,0.0000383711,0.0001714649,0.0003663705,0.00004263983,0.0008002554],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002282517,"threshold_uncertainty_score":0.004857719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1212770425309312,"score_gpt":0.4103008261045741,"score_spread":0.2890237835736428,"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."}}