{"id":"W2888587908","doi":"10.1016/j.compbiomed.2018.08.020","title":"Position-independent gesture recognition using sEMG signals via canonical correlation analysis","year":2018,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Gesture; Canonical correlation; Computer science; Classifier (UML); Gesture recognition; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Set (abstract data type); Correlation; Computer vision; Mathematics","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":[0.0001243552,0.00008259797,0.0001837821,0.0002908523,0.00006723854,0.000002804997,0.00003120374,0.00009735775,0.00003452131],"category_scores_gemma":[0.00001086905,0.00006992142,0.00002342886,0.0004032202,0.0001238532,0.0000352339,0.00001139739,0.0001111104,6.246557e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003289757,"about_ca_system_score_gemma":0.000004384245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004639213,"about_ca_topic_score_gemma":0.0001311122,"domain_scores_codex":[0.9995102,0.00003968266,0.0001562827,0.0001321451,0.0000381553,0.0001234927],"domain_scores_gemma":[0.9997621,0.00007987845,0.00002741571,0.00005688875,0.00003911146,0.00003461973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002348783,0.000148012,0.3756312,0.0001218516,0.003793743,0.00002998103,0.005044147,0.009417262,0.1066615,0.0009875265,0.004488887,0.493441],"study_design_scores_gemma":[0.001343617,0.0004540372,0.6610247,0.0001403261,0.0003708775,0.00003630419,0.00009437789,0.3313187,0.0006504258,0.003704935,0.0005714181,0.0002902065],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6836323,0.000490715,0.3147194,0.0002596842,0.0004342481,0.00007368925,0.000001590352,0.00005023234,0.000338159],"genre_scores_gemma":[0.9980313,0.000152181,0.001221976,0.0003056556,0.0002236292,0.00000309909,0.00005646957,0.000003894117,0.000001810376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4931508,"threshold_uncertainty_score":0.2851313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01708574617364733,"score_gpt":0.2810853784970677,"score_spread":0.2639996323234204,"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."}}