{"id":"W4389951994","doi":"10.1007/s42235-023-00453-8","title":"Robust Machine Learning Mapping of sEMG Signals to Future Actuator Commands in Biomechatronic Devices","year":2023,"lang":"en","type":"article","venue":"Journal of Bionic Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Robot; Kinematics; Controller (irrigation); Computer science; Model predictive control; Support vector machine; Artificial intelligence; Control theory (sociology); Engineering; Torque; SIGNAL (programming language); Artificial neural network; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004047799,0.0001494005,0.000319938,0.001254262,0.00003262097,0.00002138555,0.0001521872,0.00006320164,0.00001533064],"category_scores_gemma":[0.00003837376,0.0001404284,0.0001140652,0.001563359,0.000005789965,0.0001818129,0.00003167289,0.0003572728,0.000001846205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008678611,"about_ca_system_score_gemma":0.00001934845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005295416,"about_ca_topic_score_gemma":0.00001059308,"domain_scores_codex":[0.9989662,0.00001476134,0.0004641257,0.00007922953,0.0001938375,0.0002818984],"domain_scores_gemma":[0.9996092,0.00008024346,0.0001010564,0.00007980839,0.00005529347,0.000074391],"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":[0.000008284681,0.00001182806,0.001343874,0.000218552,0.0001615768,0.0000106965,0.0008644831,0.8326097,0.1598558,0.0000582047,0.0001984899,0.004658461],"study_design_scores_gemma":[0.003124154,0.0008257155,0.2025028,0.0026885,0.000102356,0.0001179041,0.007382031,0.6738173,0.05406512,0.0000797487,0.05386294,0.001431523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772632,0.00270001,0.01895672,0.0003778954,0.0004332677,0.00008972683,0.000002643519,0.0001272877,0.00004932019],"genre_scores_gemma":[0.997238,0.0008129369,0.001727066,0.00001189566,0.0001681623,0.000003619144,0.000002336728,0.00002831579,0.000007647531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2011589,"threshold_uncertainty_score":0.5726503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453880527681242,"score_gpt":0.2082595219871192,"score_spread":0.1937207167103068,"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."}}