{"id":"W4413847065","doi":"10.1109/tmrb.2025.3604146","title":"Personalized Myoelectric Control for Upper-Limb Exoskeletons Through Meta-Learning: A Few-Shot Learning Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Robotics and Bionics","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; Government of Alberta","keywords":"Exoskeleton; Computer science; Physical medicine and rehabilitation; Control (management); Artificial intelligence; Medicine","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":[0.0006761131,0.0007006791,0.0007453465,0.0003565448,0.0002373122,0.0005480251,0.001050828,0.000833212,0.001077389],"category_scores_gemma":[0.001548373,0.0004452204,0.000565657,0.0002656456,0.0004529305,0.0009225312,0.000918055,0.0009216302,0.0002460428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004395179,"about_ca_system_score_gemma":0.0004170682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001774931,"about_ca_topic_score_gemma":0.002696684,"domain_scores_codex":[0.9997931,0.00004877748,0.00001341348,0.00007218937,0.00004197338,0.00003055143],"domain_scores_gemma":[0.9995397,0.0002239436,0.00005582419,0.00007397513,0.00006867291,0.0000378331],"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":[0.0001636828,0.0002002507,0.00117645,0.0001150307,0.0001197155,0.0001288177,0.0001379479,0.8074456,0.0138352,0.003004631,0.0007529019,0.1729198],"study_design_scores_gemma":[0.000002982495,0.00004673306,0.0001796025,0.000004722533,0.00000883223,0.00001584306,0.000007298306,0.997129,0.001116369,0.001313297,0.000171275,0.000004096048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04653242,0.000372129,0.9507067,0.0001726637,0.00003184626,0.00004062108,0.00003159602,0.0006001199,0.001511851],"genre_scores_gemma":[0.9192657,0.0001684853,0.07825597,0.0001323359,0.00003569624,0.00008979451,0.00006977475,0.00006456581,0.001917545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001774931,"threshold_uncertainty_score":0.003604233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03415084138888712,"score_gpt":0.3110701213982944,"score_spread":0.2769192800094073,"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."}}