{"id":"W3110637195","doi":"10.18280/ria.340517","title":"EMG Signal Feature Extraction, Normalization and Classification for Pain and Normal Muscles Using Genetic Algorithm and Support Vector Machine","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Normalization (sociology); Pattern recognition (psychology); Artificial intelligence; Electromyography; Computer science; MATLAB; Feature extraction; SIGNAL (programming language); Medicine; Physical medicine and rehabilitation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004923661,0.0004712056,0.0005936572,0.0009550546,0.0002208682,0.0004783374,0.0003555571,0.0004818483,0.00107404],"category_scores_gemma":[0.001180691,0.0001444442,0.0006435755,0.0008973898,0.0001831148,0.0003799081,0.0002080715,0.0003897277,0.0003312354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002713884,"about_ca_system_score_gemma":0.0004879699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002444905,"about_ca_topic_score_gemma":0.001687384,"domain_scores_codex":[0.9995806,0.00004793981,0.00004221098,0.00009311536,0.0001893628,0.00004687737],"domain_scores_gemma":[0.9997151,0.00007428514,0.00004296863,0.00002238348,0.0001345145,0.0000105781],"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":[0.0003397484,0.0002485045,0.005773503,0.0002297196,0.00008538386,0.000218675,0.0001115164,0.03876537,0.09193049,0.001307103,0.001973016,0.859017],"study_design_scores_gemma":[0.00005566714,0.0007335722,0.03228489,0.00005531406,0.00008492468,0.0005705765,0.0001503872,0.8782128,0.08042711,0.002031631,0.005338557,0.00005450601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1775526,0.0006376342,0.8167135,0.0002329917,0.0001168838,0.0001916863,0.0003053347,0.001890905,0.002358554],"genre_scores_gemma":[0.6481702,0.0003834511,0.3463849,0.00007234529,0.00003340594,0.0003099464,0.0006841345,0.00009394733,0.003867635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002444905,"threshold_uncertainty_score":0.004861355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06108692434731836,"score_gpt":0.2918806461437128,"score_spread":0.2307937217963945,"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."}}