{"id":"W2472573003","doi":"","title":"[Recognition of walking stance phase and swing phase based on moving window].","year":2014,"lang":"en","type":"article","venue":"PubMed","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Swing; SIGNAL (programming language); Computer science; Electromyography; Phase (matter); Window (computing); Artificial intelligence; Computer vision; Speech recognition; Control theory (sociology); Physical medicine and rehabilitation; Pattern recognition (psychology); Simulation; Acoustics; Control (management); Physics; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003452695,0.0003829624,0.0002350186,0.0007537719,0.0001417805,0.0002612384,0.00031681,0.0004343346,0.002863558],"category_scores_gemma":[0.0008333573,0.0001493261,0.0002295794,0.0008005213,0.0002012437,0.0005698104,0.0001684552,0.0002026132,0.001222019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007400041,"about_ca_system_score_gemma":0.0001639794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001452167,"about_ca_topic_score_gemma":0.001803954,"domain_scores_codex":[0.999836,0.00002026758,0.00001698365,0.00004754225,0.00006599379,0.00001326594],"domain_scores_gemma":[0.9998178,0.00004557466,0.00002375649,0.00002626679,0.00007385366,0.00001281684],"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":[0.0006011832,0.00003230757,0.002530338,0.0004408837,0.00006979505,0.0004109492,0.0001298221,0.00290819,0.1937175,0.003757861,0.007826493,0.7875746],"study_design_scores_gemma":[0.0001549302,0.001300311,0.07916144,0.0002146098,0.00035944,0.005656193,0.0002830834,0.4809443,0.3127094,0.007674045,0.1113198,0.0002224436],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07905529,0.002796707,0.908596,0.0001693265,0.000600374,0.0002044307,0.0006800964,0.002088637,0.005809069],"genre_scores_gemma":[0.417913,0.002430336,0.5648963,0.000120947,0.0001716327,0.0001844789,0.002641091,0.0002013105,0.01144085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002863558,"threshold_uncertainty_score":0.009579539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588085308398767,"score_gpt":0.2144588426066712,"score_spread":0.1985779895226835,"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."}}