{"id":"W4291652749","doi":"10.1109/tim.2022.3194858","title":"A Noncontact Emotion Recognition Method Based on Complexion and Heart Rate","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Jiangxi Provincial Department of Science and Technology; National Natural Science Foundation of China","keywords":"Emotion recognition; Computer science; Artificial intelligence; Anger; Facial expression; Wearable computer; Speech recognition; Pattern recognition (psychology); Psychology; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006293774,0.0001428555,0.0001245905,0.000211805,0.0006313447,0.00007886569,0.00005106408,0.00002433164,0.0002112334],"category_scores_gemma":[0.000008682449,0.0001440495,0.00004667863,0.0001640753,0.00003154345,0.0001514273,0.000001828311,0.0002024776,0.000008168327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001841841,"about_ca_system_score_gemma":0.00003375423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000037436,"about_ca_topic_score_gemma":0.00001673793,"domain_scores_codex":[0.9981699,0.0005943684,0.0002156689,0.0003877701,0.0004865804,0.0001457384],"domain_scores_gemma":[0.9995812,0.0001196001,0.00007732308,0.0001057427,0.00003781643,0.00007835739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006356437,0.0005432962,0.000017724,0.00003815287,0.00001529846,0.000002680083,0.000795057,0.01257183,0.7491103,0.0000598695,0.0001500774,0.23606],"study_design_scores_gemma":[0.002862363,0.001959331,0.00132801,0.0000724315,0.00003836451,0.0000403743,0.0005817321,0.08296413,0.9076862,0.0002696635,0.001933299,0.0002640667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6244299,0.000005024195,0.3715512,0.001878944,0.0008951128,0.0006072947,0.00007707898,0.000100012,0.0004553843],"genre_scores_gemma":[0.9939623,0.00001063269,0.001706229,0.004126509,0.00001620371,0.0001255281,0.000005756075,0.0000124376,0.00003435732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.369845,"threshold_uncertainty_score":0.5874169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1072531132966337,"score_gpt":0.310384985572879,"score_spread":0.2031318722762453,"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."}}