{"id":"W2034345899","doi":"10.1109/tnsre.2014.2325713","title":"Enhanced Dynamic EMG-Force Estimation Through Calibration and PCI Modeling","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":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Eccentric; Isometric exercise; Concentric; Calibration; Amplitude; Control theory (sociology); Nonlinear system; Elbow; Contact force; Mathematics; Computer science; Physics; Anatomy; Structural engineering; Engineering; Medicine; Statistics; Physical therapy; Geometry; Artificial intelligence","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":[0.0008050592,0.001045838,0.0005850669,0.0004950532,0.0002403714,0.0005240921,0.0006891267,0.000664013,0.00142684],"category_scores_gemma":[0.002395258,0.0004877499,0.0004728758,0.0005605336,0.0003212748,0.001043516,0.0007277976,0.0006825666,0.0007791254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003883489,"about_ca_system_score_gemma":0.0006073097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002996532,"about_ca_topic_score_gemma":0.003049068,"domain_scores_codex":[0.9993709,0.00009565774,0.00003639258,0.0001699259,0.0002866536,0.00004055517],"domain_scores_gemma":[0.9995046,0.0002029951,0.00008990663,0.00006999192,0.0001214514,0.00001104642],"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":[0.0002227348,0.0001450034,0.00339916,0.0003276392,0.00008876576,0.0002477233,0.0002547372,0.5350962,0.1301549,0.003900008,0.001032132,0.3251311],"study_design_scores_gemma":[0.000005709005,0.00006482815,0.001754522,0.00001330796,0.00001310796,0.0001208305,0.00001200491,0.9782928,0.0177239,0.0007778936,0.001204568,0.00001650268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0132263,0.0001087636,0.9850689,0.00003845658,0.00001241235,0.00004144421,0.00003348428,0.0005817132,0.000888617],"genre_scores_gemma":[0.6768785,0.000439097,0.3186977,0.00009167714,0.00002425941,0.000231251,0.0002720779,0.0001791492,0.003186411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002996532,"threshold_uncertainty_score":0.0059582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006040032363411575,"score_gpt":0.2042517494065527,"score_spread":0.1982117170431412,"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."}}