{"id":"W2902728877","doi":"10.1109/icpr.2018.8545718","title":"CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition","year":2018,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Recurrent neural network; Gesture; Convolutional neural network; Artificial intelligence; Gesture recognition; Human skeleton; Deep learning; Pattern recognition (psychology); Skeleton (computer programming); Computer vision; Feature extraction; Artificial neural network","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":[0.0003375629,0.001581725,0.0007482615,0.0007565247,0.0001942128,0.0004418098,0.0009272065,0.0006587238,0.00518028],"category_scores_gemma":[0.0007238159,0.0003710609,0.0006132941,0.0006517963,0.0002259205,0.0007474764,0.0006078079,0.0005043595,0.002383022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005975736,"about_ca_system_score_gemma":0.000582792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01963979,"about_ca_topic_score_gemma":0.03823951,"domain_scores_codex":[0.9995496,0.00003370021,0.00002712437,0.0001560376,0.0001436595,0.00008982772],"domain_scores_gemma":[0.9998081,0.0000304709,0.00002323079,0.0000497169,0.00007436662,0.00001407087],"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":[0.0004574023,0.0001448825,0.003322572,0.0001944123,0.0001598835,0.0002456257,0.00002976597,0.08115855,0.08320426,0.001035256,0.01199226,0.818055],"study_design_scores_gemma":[0.0000198745,0.0001390595,0.005505383,0.00002992975,0.00006314833,0.0002513908,0.00002180151,0.949797,0.03775724,0.0007634252,0.005625841,0.00002593002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3381606,0.006076144,0.5904808,0.0006147718,0.0007067745,0.0003834733,0.008348901,0.0205522,0.03467624],"genre_scores_gemma":[0.7359302,0.001286185,0.2205362,0.0004056446,0.0001093205,0.0002104097,0.01032208,0.0002444476,0.0309555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01963979,"threshold_uncertainty_score":0.03905094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171564051364419,"score_gpt":0.2490488216906206,"score_spread":0.2318924165541787,"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."}}