{"id":"W4213439147","doi":"10.1109/tnsre.2022.3153252","title":"Deep Multi-Scale Fusion of Convolutional Neural Networks for EMG-Based Movement Estimation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Kinematics; Robustness (evolution); Joint (building); Motion estimation; Motion (physics); Convolutional neural network; Motion analysis","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":[0.0007873095,0.001201102,0.0007470502,0.0006078844,0.0002194921,0.0004992499,0.0009011049,0.0007528103,0.001437485],"category_scores_gemma":[0.001724936,0.0004218519,0.0006226629,0.000729834,0.0002593347,0.0008617858,0.0008323753,0.0009324379,0.0004998637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007108136,"about_ca_system_score_gemma":0.000614708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008300009,"about_ca_topic_score_gemma":0.009191944,"domain_scores_codex":[0.9997039,0.00003613801,0.00001777269,0.00008795404,0.00009139287,0.00006273897],"domain_scores_gemma":[0.9996401,0.0001083932,0.00005150463,0.00005386742,0.0001241757,0.00002191361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004148881,0.0001979491,0.00275995,0.0001528744,0.0002115505,0.0001443097,0.00006481096,0.4101965,0.0459726,0.001837353,0.003514495,0.5345328],"study_design_scores_gemma":[0.000003183817,0.00002272137,0.0008159351,0.00000573793,0.00001663627,0.00001535435,0.0000041277,0.9925046,0.005677085,0.000553598,0.000374329,0.000006693111],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06908843,0.001103516,0.924486,0.0002232681,0.0001471179,0.00004948902,0.0002870282,0.002593898,0.002021217],"genre_scores_gemma":[0.8418465,0.0006521693,0.1522887,0.0001850647,0.00007832082,0.00008445691,0.0007927205,0.000116614,0.003955518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008300009,"threshold_uncertainty_score":0.01650339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008018500023468537,"score_gpt":0.2065218383480424,"score_spread":0.1985033383245739,"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."}}