{"id":"W4402723172","doi":"10.3389/fbioe.2024.1463377","title":"Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition","year":2024,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Computer science; Machine learning; Artificial intelligence; Control (management); Set (abstract data type); Gesture recognition; Human–computer interaction; Data mining; Pattern recognition (psychology)","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":[0.001942727,0.001213475,0.001061159,0.0005084271,0.0003441281,0.001047022,0.001336793,0.001069403,0.002009665],"category_scores_gemma":[0.005597304,0.0004031663,0.00110289,0.0005724137,0.0006971516,0.0011973,0.001775086,0.001865981,0.0009844159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003982032,"about_ca_system_score_gemma":0.0004827729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003699919,"about_ca_topic_score_gemma":0.00631833,"domain_scores_codex":[0.9987584,0.0004292704,0.00008051074,0.0003750222,0.0002376259,0.0001191834],"domain_scores_gemma":[0.9977297,0.00120639,0.0001170076,0.0005622544,0.0002677033,0.0001168609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002860954,0.001228974,0.03835443,0.001272727,0.0006499044,0.0009249547,0.0009354741,0.3204376,0.03261426,0.006502286,0.02083104,0.5733874],"study_design_scores_gemma":[0.00005155087,0.0004347776,0.02216742,0.0001178817,0.00008891388,0.0003855876,0.0002462062,0.9490252,0.0123744,0.00775608,0.007262229,0.00008976914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3093153,0.003966318,0.6706729,0.001466372,0.0007778839,0.0002954083,0.005877978,0.003225778,0.004402098],"genre_scores_gemma":[0.9121941,0.0008978834,0.07274146,0.000370969,0.0001478912,0.000303313,0.009966664,0.0001378389,0.003239983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003699919,"threshold_uncertainty_score":0.01027423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02779323003649172,"score_gpt":0.2150958421365791,"score_spread":0.1873026121000874,"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."}}