{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006692851,0.00008188757,0.00009525098,0.00007659131,0.00002513197,0.00002596371,0.00003713034,0.00002930442,0.006699366],"category_scores_gemma":[0.00002272182,0.00007145837,0.0000485303,0.00008764116,0.000006886416,0.0000626369,0.000003206304,0.00004499752,0.001989692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001386379,"about_ca_system_score_gemma":0.000003272315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004728073,"about_ca_topic_score_gemma":0.000009871677,"domain_scores_codex":[0.9996313,0.00001100835,0.00009825409,0.00007704504,0.00008908386,0.00009327212],"domain_scores_gemma":[0.9997994,0.0000247973,0.00001012766,0.0001025769,0.00001642395,0.00004664579],"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":[0.0000537156,0.0002125693,0.001572641,0.0002792117,0.000199459,0.000009278898,0.0001159918,0.7305861,0.01842355,0.002910521,0.06592112,0.1797159],"study_design_scores_gemma":[0.0001544519,0.00004723825,0.001129313,0.000006385436,0.00001642287,8.25734e-7,0.000001655736,0.9864064,0.00619026,0.00008390531,0.005863862,0.0000992342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04144417,0.000008285351,0.4276431,0.001345469,0.0002549608,0.0001775928,0.00003725808,0.002046917,0.5270423],"genre_scores_gemma":[0.9920468,9.512831e-7,0.004170589,0.0002618407,0.00003319995,0.000006474178,0.0001464545,0.00001558776,0.003318049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9506027,"threshold_uncertainty_score":0.9987874,"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."}}