{"id":"W4292862515","doi":"10.1109/memea54994.2022.9856558","title":"Multimodal Physiological Signals and Machine Learning for Stress Detection by Wearable Devices","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Wearable computer; Machine learning; Artificial intelligence; Smartwatch; Support vector machine; Wearable technology; Naive Bayes classifier; Random forest; Modalities; Embedded system","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":[0.001225218,0.001036663,0.000856258,0.00131022,0.000213763,0.0007795838,0.0004761508,0.0008586956,0.001110123],"category_scores_gemma":[0.003179999,0.0001759578,0.0007233528,0.001238078,0.0002552913,0.0009542151,0.0005386585,0.0006731161,0.000697223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002288856,"about_ca_system_score_gemma":0.0002357513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008904278,"about_ca_topic_score_gemma":0.001178834,"domain_scores_codex":[0.9990147,0.0003437807,0.00007421908,0.0002319053,0.0002752263,0.00006011155],"domain_scores_gemma":[0.9992536,0.0002881047,0.0001415937,0.0001000508,0.0001847877,0.00003180875],"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":[0.0004419295,0.0004209062,0.02571512,0.0005563476,0.0003651948,0.000337961,0.000145673,0.03977723,0.03654033,0.001635285,0.005417576,0.8886465],"study_design_scores_gemma":[0.00003572203,0.00100785,0.07439601,0.0002467244,0.0002300238,0.0006188201,0.000261575,0.8777941,0.02964266,0.0075952,0.008056939,0.000114406],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3217736,0.02020509,0.645035,0.002207589,0.0007613155,0.0002373486,0.001595806,0.002467477,0.005716713],"genre_scores_gemma":[0.8775414,0.004680563,0.1133831,0.0003613748,0.0005147444,0.0002400757,0.001252226,0.00004729684,0.001979104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00131022,"threshold_uncertainty_score":0.006479621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0570145403156947,"score_gpt":0.3311059234763353,"score_spread":0.2740913831606406,"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."}}