{"id":"W2333636101","doi":"10.1109/embc.2014.6945095","title":"Foot gait time series estimation based on support vector machine","year":2014,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Research Chairs","keywords":"STRIDE; Series (stratigraphy); Standard deviation; Interval (graph theory); Gait; Algorithm; Time series; SIGNAL (programming language); Support vector machine; Computer science; Foot (prosody); Artificial intelligence; Mathematics; Pattern recognition (psychology); Statistics; Machine learning; Combinatorics; Physical medicine and rehabilitation","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":[0.0005574872,0.0004777714,0.0008270458,0.00104941,0.0001842917,0.0005419579,0.000612992,0.0005649619,0.0009088265],"category_scores_gemma":[0.00230992,0.0002230328,0.000349088,0.001008921,0.0001952995,0.0007476948,0.000287129,0.0006853937,0.000427126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001820301,"about_ca_system_score_gemma":0.0002821868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001579693,"about_ca_topic_score_gemma":0.001049589,"domain_scores_codex":[0.9995525,0.00006973396,0.00004033822,0.0001096873,0.0001937633,0.00003391882],"domain_scores_gemma":[0.9991786,0.0003826146,0.0001007201,0.0000489643,0.0002613067,0.00002771942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002096628,0.00009193084,0.003360554,0.0001555544,0.00008496035,0.0001616014,0.0000713624,0.1122501,0.02387844,0.002307574,0.001450075,0.8559783],"study_design_scores_gemma":[0.000006026176,0.00005879598,0.00135641,0.000007521953,0.000008973164,0.00007890254,0.000007722187,0.9934403,0.003838015,0.0005220083,0.000664234,0.00001101754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02245154,0.0002677065,0.9762577,0.00003644013,0.00005161278,0.00002587451,0.00005019825,0.0005530844,0.0003057992],"genre_scores_gemma":[0.4767966,0.000402114,0.5206531,0.00003485476,0.00008495921,0.0001265123,0.0003264659,0.00005403984,0.001521365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001579693,"threshold_uncertainty_score":0.003140986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004618389501231707,"score_gpt":0.1878545279926259,"score_spread":0.1832361384913942,"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."}}