{"id":"W4386011151","doi":"10.1007/s00500-023-09077-w","title":"A study on hand gesture recognition algorithm realized with the aid of efficient feature extraction method and convolution neural networks: design and its application to VR environment","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea","keywords":"Gesture; Computer science; Gesture recognition; Artificial intelligence; Convolutional neural network; Feature extraction; Feature (linguistics); Computer vision; Motion (physics); Pattern recognition (psychology); Speech recognition","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":[0.001511828,0.0001719604,0.0002110281,0.0001278828,0.0003451755,0.000116014,0.0001434469,0.00007628436,2.446662e-7],"category_scores_gemma":[0.00003073885,0.0001208637,0.00002295556,0.000536504,0.00002736073,0.00008583611,0.0001160689,0.0001997111,0.000006018383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003630772,"about_ca_system_score_gemma":0.00001202973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008932813,"about_ca_topic_score_gemma":0.00000244808,"domain_scores_codex":[0.9981819,0.00055878,0.0002140321,0.0005055112,0.0003261952,0.0002135573],"domain_scores_gemma":[0.9987225,0.0006644951,0.0002194432,0.0002268182,0.00008861501,0.00007814394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008220136,0.0001133691,0.0001606446,0.00002540212,0.00004836763,0.00000872593,0.005687697,0.2857884,0.001700615,0.00003099856,0.0001307201,0.7062229],"study_design_scores_gemma":[0.0007954912,0.0004885307,0.01074996,0.00007799565,0.00003134234,0.0000638794,0.0004938107,0.9863424,0.0006254128,0.00003578555,0.0001417878,0.0001536179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1147725,0.0001235944,0.8827102,0.0005652988,0.0001023816,0.001584325,0.000002549149,0.0001314969,0.000007718903],"genre_scores_gemma":[0.9758506,0.000007226448,0.02382229,0.00008524088,0.0001116276,0.00008738234,0.00001024689,0.00001336218,0.00001201265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8610781,"threshold_uncertainty_score":0.4928679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02955957871537686,"score_gpt":0.2848866008191272,"score_spread":0.2553270221037504,"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."}}