{"id":"W2111697356","doi":"10.1109/tsp.2008.925246","title":"A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hidden Markov model; Autoregressive model; Multivariate statistics; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition; Multivariate analysis; Electromyography; Machine learning; Physical medicine and rehabilitation; Mathematics; Statistics; Medicine","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":[0.001445084,0.001012242,0.000696619,0.0008922367,0.0002698676,0.0008875626,0.001077229,0.0008132376,0.001396258],"category_scores_gemma":[0.002772914,0.0003696944,0.001235647,0.0008890523,0.0004746873,0.0009500631,0.0006223957,0.001221034,0.0006831159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004794821,"about_ca_system_score_gemma":0.0009461936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005573359,"about_ca_topic_score_gemma":0.007359295,"domain_scores_codex":[0.999303,0.0003074692,0.00004189523,0.0001791111,0.0001256624,0.0000428044],"domain_scores_gemma":[0.9993331,0.0003754343,0.00009728636,0.00006048199,0.0001033524,0.00003025719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001254739,0.000135066,0.002419756,0.0003575073,0.0002838038,0.0004133582,0.0002193668,0.6470194,0.0221775,0.08349031,0.002956587,0.2404018],"study_design_scores_gemma":[0.000003011034,0.00003783834,0.0004989337,0.00001378282,0.00001938983,0.00005814084,0.00001059745,0.9877725,0.0005531701,0.009473715,0.001543819,0.00001502007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001314551,0.0002621973,0.9977264,0.00007595935,0.00002523792,0.00002007148,0.00007780413,0.0001919966,0.0003057908],"genre_scores_gemma":[0.1561349,0.001869321,0.836405,0.0001219581,0.00023565,0.0003199558,0.0007495261,0.00015809,0.004005636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005573359,"threshold_uncertainty_score":0.01108181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04437351608533818,"score_gpt":0.285465269366276,"score_spread":0.2410917532809378,"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."}}