{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002718164,0.0005105121,0.0002738488,0.000321745,0.0001295433,0.0004494476,0.0001861051,0.0005785578,0.001670376],"category_scores_gemma":[0.001191436,0.0001221806,0.0001556651,0.000338205,0.0001448418,0.0004052609,0.0002911093,0.000250213,0.000598416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009806349,"about_ca_system_score_gemma":0.0001787704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001453707,"about_ca_topic_score_gemma":0.002661572,"domain_scores_codex":[0.999851,0.00003886421,0.00001089897,0.00003927301,0.00004117735,0.00001882987],"domain_scores_gemma":[0.9998252,0.00008643049,0.00001716843,0.00001396841,0.00004755221,0.000009708836],"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":[0.001098312,0.0001310766,0.008832927,0.0003574014,0.0000993827,0.0005618752,0.0002130507,0.01802187,0.3374521,0.0004594927,0.001737282,0.6310351],"study_design_scores_gemma":[0.0001051568,0.0009194837,0.1171876,0.0001911573,0.0002050375,0.003665623,0.0003796552,0.6767639,0.1928572,0.002463758,0.005152246,0.0001092615],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5974063,0.002170809,0.3928512,0.0003977276,0.0001541957,0.0001167035,0.0007227688,0.000984047,0.005196318],"genre_scores_gemma":[0.9567819,0.0006077178,0.03901603,0.00008990087,0.00004750359,0.00004296477,0.0001776955,0.00006349903,0.003172895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001670376,"threshold_uncertainty_score":0.005587995,"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."}}