{"id":"W3133378119","doi":"10.3389/fbioe.2021.642742","title":"Real-Time and Dynamically Consistent Estimation of Muscle Forces Using a Moving Horizon EMG-Marker Tracking Algorithm—Application to Upper Limb Biomechanics","year":2021,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Hôpital du Sacré-Cœur de Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Kinematics; Estimator; Computation; Electromyography; Torque; Control theory (sociology); Noise (video); Tracking (education); Biomechanics; Algorithm; Simulation; Mathematics; Artificial intelligence; Physical medicine and rehabilitation; Physics; Control (management)","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":[0.0008903926,0.0004313021,0.0005562296,0.0002871896,0.0002168401,0.0003196235,0.0004428345,0.0006886251,0.0007472899],"category_scores_gemma":[0.001935505,0.0002740089,0.000279505,0.0002002869,0.0002791178,0.0003422824,0.0004368472,0.0003991203,0.0001179991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949484,"about_ca_system_score_gemma":0.0008007815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005988681,"about_ca_topic_score_gemma":0.003230727,"domain_scores_codex":[0.9997931,0.00005914156,0.00001491904,0.00004372181,0.0000689539,0.00002014227],"domain_scores_gemma":[0.9995471,0.0002528597,0.00006467108,0.00003016271,0.0000832507,0.00002191383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001681481,0.00006398535,0.0008179095,0.00009383708,0.00003623674,0.00008061367,0.00006712952,0.8512733,0.01748134,0.002001484,0.0002718098,0.1276442],"study_design_scores_gemma":[0.000005097944,0.00002884034,0.0001885493,0.000003208146,0.000002589408,0.000008680401,0.000002934346,0.9983506,0.001095928,0.0001919407,0.0001178243,0.000003801869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03566057,0.0001519049,0.9631051,0.00009011848,0.00001495908,0.00003609385,0.00001292013,0.0003427939,0.0005855178],"genre_scores_gemma":[0.7032093,0.0001113388,0.2955234,0.00003570504,0.00001125422,0.00008751162,0.00003042831,0.00003364671,0.0009575253],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005988681,"threshold_uncertainty_score":0.01190764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004628547660779969,"score_gpt":0.200175282070426,"score_spread":0.195546734409646,"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."}}