{"id":"W2886903801","doi":"10.3390/bdcc2030021","title":"EMG Pattern Recognition in the Era of Big Data and Deep Learning","year":2018,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":255,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Big data; Computer science; Feature (linguistics); Feature engineering; Artificial intelligence; Deep learning; Pattern recognition (psychology); Data science; Data analysis; Machine learning; SIGNAL (programming language); Signal processing; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003373369,0.00009344628,0.0001160728,0.00006659698,0.0001340418,0.00004207601,0.0001867327,0.0000273332,0.000002466934],"category_scores_gemma":[0.0001566594,0.0000786,0.000007080856,0.0002072108,0.00008605004,0.0001273904,0.0003105105,0.0001890398,8.712257e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002305454,"about_ca_system_score_gemma":0.000004080866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004214199,"about_ca_topic_score_gemma":0.0001330993,"domain_scores_codex":[0.9993246,0.00006616001,0.0001538062,0.0002267153,0.00008154006,0.0001472078],"domain_scores_gemma":[0.9994036,0.0002850821,0.00004415176,0.0001939082,0.00005514151,0.0000181251],"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":[0.000005046253,0.000009938091,0.01395789,0.00003637412,0.00003755042,0.000001208487,0.001202411,4.535371e-7,0.0001180524,8.655517e-7,0.00006511226,0.9845651],"study_design_scores_gemma":[0.001298239,0.000190801,0.8141217,0.0007545229,0.0001112636,0.00002630559,0.009204043,0.1701103,0.0005625093,0.0001503758,0.00305234,0.0004176252],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9073659,0.001157068,0.09016441,0.00006284877,0.0001393932,0.0001192273,0.0001146635,0.00004088706,0.000835631],"genre_scores_gemma":[0.9985323,0.0004739028,0.0001058547,0.0001209865,0.0002947134,0.000001575853,0.0004614163,0.000008777496,4.714076e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9841475,"threshold_uncertainty_score":0.3205215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08606349209004238,"score_gpt":0.2729631672584356,"score_spread":0.1868996751683932,"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."}}