{"id":"W4200267796","doi":"10.3389/fncom.2021.759489","title":"InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling","year":2021,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Inverse dynamics; Torque; Computer science; Acceleration; Joint (building); Control theory (sociology); Electromyography; Redundancy (engineering); Recurrent neural network; Artificial intelligence; Simulation; Artificial neural network; Engineering; Physical medicine and rehabilitation; Kinematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008333919,0.001194468,0.001217273,0.0006529814,0.0003405292,0.0008963972,0.001694231,0.001575631,0.003385419],"category_scores_gemma":[0.001733638,0.0006567846,0.0009874499,0.0006858743,0.0005288299,0.001030969,0.001109441,0.001166815,0.0007905347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005349375,"about_ca_system_score_gemma":0.001324151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006191787,"about_ca_topic_score_gemma":0.007103202,"domain_scores_codex":[0.9996595,0.00008122664,0.000019155,0.00009343539,0.0001077608,0.00003887756],"domain_scores_gemma":[0.9995492,0.0001956847,0.00005484185,0.00005674169,0.0001216638,0.00002182827],"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":[0.00004893751,0.00005247655,0.0003725908,0.000102917,0.0000731108,0.00009386144,0.00004025927,0.8924129,0.003025154,0.01112075,0.001445767,0.09121128],"study_design_scores_gemma":[0.0000024569,0.0000115398,0.00003005513,0.000003146126,0.000002774474,0.0000111817,0.000001647526,0.9976848,0.0002606442,0.001501619,0.000487407,0.000002655921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003495077,0.0001335339,0.9941425,0.0001000982,0.00004296624,0.00002037787,0.00003268325,0.0006423328,0.001390532],"genre_scores_gemma":[0.3008184,0.0003116086,0.6877881,0.0003234531,0.000156748,0.0003825939,0.0004385997,0.0005062745,0.009274178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006191787,"threshold_uncertainty_score":0.01231152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166356676743056,"score_gpt":0.219804546966797,"score_spread":0.2031688792924914,"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."}}