{"id":"W3181898599","doi":"10.1101/2021.07.07.451532","title":"MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canada Research Chairs","keywords":"Kinematics; Convolutional neural network; Computer science; Artificial intelligence; Artificial neural network; Torque; Exoskeleton; Electromyography; Joint (building); Recurrent neural network; Pattern recognition (psychology); Machine learning; Engineering; Simulation","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":[0.0007865491,0.001050898,0.0005083222,0.0004948237,0.0001363734,0.0006059128,0.000819686,0.0007327914,0.002451489],"category_scores_gemma":[0.001924586,0.0003504359,0.0007187096,0.0004759414,0.0002327219,0.000850783,0.0005128894,0.000597343,0.000763353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004334027,"about_ca_system_score_gemma":0.0005327868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004566467,"about_ca_topic_score_gemma":0.004752917,"domain_scores_codex":[0.9997935,0.00004790792,0.00001289384,0.00008213698,0.0000466579,0.00001703068],"domain_scores_gemma":[0.9996583,0.0001649743,0.00005090896,0.00004244477,0.00007282884,0.00001058906],"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":[0.0001382782,0.0001254594,0.002517086,0.0002109257,0.0002349718,0.0001221254,0.0000440417,0.6840381,0.01454128,0.002814884,0.003101219,0.2921117],"study_design_scores_gemma":[0.000002792145,0.0000292321,0.0003722795,0.000008432417,0.00000786983,0.00001621256,0.00000281036,0.9966086,0.001710509,0.000750971,0.0004863355,0.00000390947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03848508,0.0009333435,0.9530187,0.0002149492,0.0001521849,0.00007100052,0.0003202016,0.005217564,0.001586974],"genre_scores_gemma":[0.6565494,0.0009315513,0.3323703,0.0002072461,0.0001315428,0.0003738444,0.00117214,0.0003485608,0.007915521],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004566467,"threshold_uncertainty_score":0.009079754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006119774173196585,"score_gpt":0.1862994923597476,"score_spread":0.180179718186551,"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."}}