{"id":"W2130568999","doi":"10.1109/mmsp.2004.1436543","title":"Gait recognition using dynamic time warping","year":2005,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dynamic time warping; Gait; Image warping; Computer science; Gait cycle; Artificial intelligence; Sequence (biology); Pattern recognition (psychology); Computer vision; Physical medicine and rehabilitation; Medicine; Kinematics","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":[0.0005793938,0.000908446,0.0009437015,0.002460354,0.0002576389,0.0007433486,0.0009907174,0.0006027567,0.002223603],"category_scores_gemma":[0.001632926,0.0003836336,0.0006176432,0.002168661,0.000437793,0.001199917,0.0007110169,0.0005991631,0.002204753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001559199,"about_ca_system_score_gemma":0.000309877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000804897,"about_ca_topic_score_gemma":0.00111657,"domain_scores_codex":[0.9993519,0.00008511116,0.00006643381,0.0002129445,0.0002385795,0.00004493216],"domain_scores_gemma":[0.999419,0.0001205012,0.0001198789,0.0001462912,0.0001626193,0.00003171967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001253119,0.000114064,0.00145516,0.0001587136,0.0001013228,0.0002871114,0.00004904763,0.01665697,0.07638946,0.003085548,0.003732511,0.8978447],"study_design_scores_gemma":[0.00005980446,0.0008172708,0.0102758,0.0001021121,0.0001370858,0.004242171,0.0001301127,0.7789961,0.1583325,0.01728145,0.02943249,0.0001930558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007980602,0.0003158749,0.9896702,0.0000358311,0.00007127109,0.0000894158,0.0001866958,0.001071615,0.0005786123],"genre_scores_gemma":[0.1154062,0.0009255027,0.8793774,0.00009985577,0.0001319699,0.0002401463,0.001299099,0.00016983,0.002350113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002460354,"threshold_uncertainty_score":0.007438719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01308151416496458,"score_gpt":0.2147515818978908,"score_spread":0.2016700677329262,"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."}}