{"id":"W4410790608","doi":"10.1016/j.neunet.2025.107605","title":"Real-time fine finger motion decoding for transradial amputees with surface electromyography","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Electromyography; Decoding methods; Computer science; Motion (physics); Artificial intelligence; Surface (topology); Computer vision; Physical medicine and rehabilitation; Speech recognition; Medicine; Mathematics; Algorithm; Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00006594268,0.0001966656,0.0002171087,0.0001268826,0.0001618363,0.00004686144,0.00009492745,0.00008174397,0.00001170481],"category_scores_gemma":[0.000004679184,0.0001791319,0.0001186246,0.0006636935,0.00002927972,0.000119455,0.000006219884,0.0001647117,2.235172e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002581531,"about_ca_system_score_gemma":0.000005698562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001130746,"about_ca_topic_score_gemma":0.00003662482,"domain_scores_codex":[0.999119,0.00001775297,0.0001736872,0.0002083383,0.00007975927,0.0004014117],"domain_scores_gemma":[0.9996039,0.000154732,0.00002581139,0.0001285988,0.00004554715,0.00004141719],"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":[0.0002305387,0.00004650854,0.002820059,0.0001149333,0.0004771152,0.000002171068,0.000141416,0.7628757,0.01975164,0.0008339912,0.02276355,0.1899424],"study_design_scores_gemma":[0.0008692603,0.0001863645,0.01418751,0.00005026118,0.00007077225,0.000002004344,0.00001073578,0.9803401,0.002576787,0.0001557334,0.001246376,0.0003040756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7892386,0.0005757374,0.2060502,0.000499909,0.0003827373,0.0005495587,0.000005435538,0.0008178194,0.001880057],"genre_scores_gemma":[0.996969,0.0002333846,0.002389525,0.00008163513,0.0001472818,0.0000482036,0.00002996416,0.00002974179,0.00007124635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2174644,"threshold_uncertainty_score":0.7304788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005534966288332719,"score_gpt":0.2032668003027472,"score_spread":0.1977318340144144,"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."}}