{"id":"W2063152058","doi":"10.1109/tnsre.2013.2247421","title":"Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Normalization (sociology); Computer science; Artificial intelligence; Kinematics; Pattern recognition (psychology); Control theory (sociology); Control (management)","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":[0.0002217289,0.0002126414,0.000262834,0.0003013792,0.0001581955,0.00004788898,0.00003985569,0.00008879918,0.000005771554],"category_scores_gemma":[0.00003540278,0.0002031118,0.0001414733,0.000212688,0.00002499517,0.0001610249,1.555353e-7,0.000147137,9.157887e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006121986,"about_ca_system_score_gemma":0.000006813058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000188552,"about_ca_topic_score_gemma":0.000005237858,"domain_scores_codex":[0.9989842,0.00004538703,0.000377307,0.0002201152,0.0001204987,0.0002524953],"domain_scores_gemma":[0.9989326,0.0006852685,0.00005563301,0.0001093985,0.0001370935,0.00007999414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006328485,0.0000501975,0.00005947717,0.0004490619,0.0001030437,8.025977e-8,0.00005560172,0.8847898,0.01621396,0.00002388929,0.00004908137,0.09814254],"study_design_scores_gemma":[0.002530365,0.0003931032,0.002545153,0.00005076494,0.00004779085,0.000002680204,0.00001798038,0.9930606,0.0007894045,0.00001369639,0.0003149369,0.0002334901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05723993,0.00005879387,0.9402036,0.0003058559,0.0007059087,0.00101691,0.00007314797,0.0003851558,0.00001070838],"genre_scores_gemma":[0.9983028,0.00000766093,0.000510046,0.00007138869,0.00009477446,0.0009531891,0.00001415027,0.00003904632,0.000006975316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9410629,"threshold_uncertainty_score":0.8282661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005493986499818748,"score_gpt":0.185595571492695,"score_spread":0.1801015849928763,"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."}}