{"id":"W4319161334","doi":"10.20944/preprints202302.0052.v1","title":"Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review","year":2023,"lang":"en","type":"review","venue":"Preprints.org","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"SIGNAL (programming language); Computer science; Noise (video); Interference (communication); Noise reduction; Subtraction; Channel (broadcasting); Artificial intelligence; Pattern recognition (psychology); Signal processing; Time domain; Speech recognition; Computer vision; Telecommunications; Mathematics","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":[0.001568687,0.001179508,0.001912649,0.005538696,0.0003453664,0.001334494,0.001225758,0.001420423,0.003659297],"category_scores_gemma":[0.002994242,0.0005209761,0.00109756,0.003997695,0.0007154434,0.00179669,0.0007154758,0.0009765872,0.002619124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004723346,"about_ca_system_score_gemma":0.001557325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001450603,"about_ca_topic_score_gemma":0.001765238,"domain_scores_codex":[0.9993132,0.0001106795,0.0001676969,0.000129525,0.0002452719,0.00003351205],"domain_scores_gemma":[0.997689,0.0014571,0.0002431797,0.00006294001,0.0004912096,0.00005648883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007097256,0.00006885577,0.0002420328,0.05204623,0.0001749804,0.0001376,0.00009035551,0.0003389756,0.002280947,0.001676128,0.009359813,0.933513],"study_design_scores_gemma":[0.00002233974,0.0002805876,0.00312733,0.0222043,0.000814535,0.002433904,0.0001830056,0.0003434,0.003213044,0.002659593,0.9646239,0.00009409824],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001546148,0.9984157,0.0005778085,0.0001290277,0.0001177788,0.00001050649,0.00003177316,0.00001522792,0.0005476158],"genre_scores_gemma":[0.000915504,0.997481,0.0009590553,0.0001008624,0.0001106666,0.00001609318,0.00005730482,0.000006902923,0.0003526958],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005538696,"threshold_uncertainty_score":0.0122416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2410404327624064,"score_gpt":0.3588901554570063,"score_spread":0.1178497226945998,"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."}}