{"id":"W2007041320","doi":"10.1109/t-affc.2013.29","title":"Body Movements for Affective Expression: A Survey of Automatic Recognition and Generation","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":217,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Representation (politics); Affective computing; Expression (computer science); Computer science; Emotion recognition; Notation; Movement (music); State (computer science); Artificial intelligence; Psychology; Human–computer interaction; Algorithm; Mathematics","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":[0.0004502985,0.0001769473,0.0002373447,0.000214408,0.0002294089,0.00003150969,0.00004819243,0.0001288023,0.0003205262],"category_scores_gemma":[0.00004182718,0.0001752137,0.00008522235,0.0001961906,0.00005379386,0.0001578709,0.000001676268,0.0001548267,0.00007990549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005659469,"about_ca_system_score_gemma":0.00001502469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003019132,"about_ca_topic_score_gemma":0.00008309729,"domain_scores_codex":[0.9984765,0.0005328491,0.0002902156,0.000371099,0.0001234449,0.000205874],"domain_scores_gemma":[0.9985437,0.0006952345,0.0001970082,0.0001386195,0.0003547878,0.00007065719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001192344,0.001075542,0.0004721249,0.000133758,0.0002812413,7.312768e-7,0.003199969,0.0003501705,0.08100475,0.00001628806,0.0006347907,0.9127114],"study_design_scores_gemma":[0.008787385,0.003382703,0.3792449,0.0008359777,0.0002024065,0.00003378036,0.001700548,0.1895466,0.4137776,0.001521311,0.00001334812,0.0009534258],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5317373,0.00001249628,0.4662427,0.00001178569,0.0006326693,0.0009856627,0.00005725757,0.00005825598,0.0002618949],"genre_scores_gemma":[0.9978535,0.000005190524,0.001489039,0.00009186262,0.00006762994,0.0002967482,0.00006368264,0.00002715444,0.0001052556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.911758,"threshold_uncertainty_score":0.7145007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07314432565335487,"score_gpt":0.3265852622291428,"score_spread":0.2534409365757879,"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."}}