{"id":"W3006003538","doi":"10.3389/fbioe.2020.00058","title":"Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition","year":2020,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Gesture; Identification (biology); Liveness; Gesture recognition; Speech recognition; Artificial intelligence; 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":[0.001585034,0.0004217511,0.0004887023,0.0004093764,0.0001625038,0.0005651469,0.0004285458,0.0008043315,0.001286405],"category_scores_gemma":[0.00525946,0.0001672419,0.0001981037,0.000251284,0.0004146581,0.0009281854,0.000606456,0.0003178683,0.0005716569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000117387,"about_ca_system_score_gemma":0.0002289195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003084757,"about_ca_topic_score_gemma":0.0005173516,"domain_scores_codex":[0.9983335,0.0005442905,0.00007612134,0.0003190124,0.0006557994,0.00007132535],"domain_scores_gemma":[0.9981498,0.00103425,0.0001990644,0.0001883127,0.0003801366,0.0000484183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001007055,0.0001159946,0.02174713,0.0004072924,0.00007131396,0.0002417534,0.0001884331,0.001481863,0.6186495,0.0008605368,0.0004759844,0.3547531],"study_design_scores_gemma":[0.00007289212,0.005104239,0.2153573,0.0002201345,0.0002592413,0.007232308,0.0004806395,0.08595958,0.6738772,0.001903201,0.009334851,0.0001983939],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7760048,0.004133932,0.2126503,0.0004625448,0.00024958,0.0001889372,0.0002611723,0.0004397091,0.005608954],"genre_scores_gemma":[0.957853,0.0007729196,0.03970402,0.00007710132,0.00005456738,0.00005419269,0.00008660281,0.0000226029,0.001375042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001585034,"threshold_uncertainty_score":0.008382559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416441974158155,"score_gpt":0.2040606840640346,"score_spread":0.189896264322453,"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."}}