{"id":"W2767223895","doi":"10.1016/j.cmpb.2017.10.024","title":"Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms","year":2017,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":192,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Indian Institute of Technology Madras","keywords":"Electromyography; Computer science; Muscle fatigue; Algorithm; Artificial intelligence; Surface (topology); Pattern recognition (psychology); Speech recognition; Machine learning; Physical medicine and rehabilitation; Medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000956806,0.0007173154,0.0006496345,0.001296601,0.0002374054,0.0006142808,0.0005523315,0.0009552558,0.002087052],"category_scores_gemma":[0.002527378,0.0002693176,0.0005677414,0.00116086,0.0002251472,0.001221664,0.0003852094,0.0006113184,0.0008596072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710405,"about_ca_system_score_gemma":0.00027927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008258389,"about_ca_topic_score_gemma":0.001188525,"domain_scores_codex":[0.9995311,0.0001027782,0.00004590943,0.0001138213,0.0001806278,0.00002580995],"domain_scores_gemma":[0.9989165,0.0006447819,0.0001077536,0.00007700674,0.0002315215,0.00002249589],"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":[0.000292595,0.0002342255,0.00272038,0.0004272083,0.0001397459,0.0001048705,0.0001000278,0.02193516,0.1227374,0.001523584,0.0008303184,0.8489546],"study_design_scores_gemma":[0.00006535642,0.0003657452,0.01976858,0.00006708789,0.0001388603,0.0008647447,0.00005996024,0.9150436,0.05784463,0.002624443,0.003084245,0.00007280905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01805574,0.0006389254,0.9801825,0.00004236565,0.00004270591,0.00005437523,0.00006195679,0.000387262,0.0005341702],"genre_scores_gemma":[0.1774883,0.0007918538,0.8191398,0.00007204862,0.00008073827,0.0001478971,0.0001799977,0.00007788582,0.002021538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002087052,"threshold_uncertainty_score":0.006981909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05717480540799271,"score_gpt":0.3380732247201203,"score_spread":0.2808984193121276,"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."}}