{"id":"W4382515262","doi":"10.1002/aisy.202300050","title":"EmoSense: Revealing True Emotions Through Microgestures","year":2023,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Hong Kong Government; Hong Kong Polytechnic University","keywords":"Stress (linguistics); Emotion detection; Capacitive sensing; Computer science; Random forest; Wearable computer; Artificial intelligence; Emotion recognition; 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":[0.0001526108,0.0004861105,0.0002870286,0.0002502443,0.0001292581,0.0004592135,0.0002855011,0.0004568486,0.002766078],"category_scores_gemma":[0.0004648702,0.0001515706,0.0002170006,0.0001586309,0.0001883938,0.0004624204,0.0004721458,0.0001949579,0.0005383129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007660998,"about_ca_system_score_gemma":0.00004675864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001794311,"about_ca_topic_score_gemma":0.000548203,"domain_scores_codex":[0.999871,0.0000215561,0.000007555022,0.0000362333,0.00004953856,0.00001413741],"domain_scores_gemma":[0.999908,0.00002974452,0.0000213443,0.0000120643,0.00001886626,0.00001000501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001259367,0.000181979,0.01381649,0.0005599426,0.0001098918,0.0007058604,0.000569096,0.002023098,0.7453877,0.00099625,0.006503606,0.2278867],"study_design_scores_gemma":[0.0002632499,0.005536793,0.3151856,0.0003028543,0.0005583453,0.006138739,0.001759845,0.1298208,0.4899432,0.006028122,0.04414415,0.0003182582],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7563921,0.002273473,0.2223536,0.0005822853,0.0005478839,0.0002705913,0.00152485,0.002834704,0.01322045],"genre_scores_gemma":[0.9435151,0.0009750778,0.04637764,0.0005897813,0.0001452617,0.0001899022,0.0004432618,0.0001213856,0.007642781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002766078,"threshold_uncertainty_score":0.009253442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06817276192545263,"score_gpt":0.3691302568140731,"score_spread":0.3009574948886205,"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."}}