{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00004857106,0.00007974686,0.00008873927,0.0001087594,0.0000365796,0.00002833421,0.00003498952,0.00004036568,0.00392341],"category_scores_gemma":[0.000005240182,0.00008162548,0.00006177914,0.0001458894,0.00000667486,0.0001518468,0.000006407817,0.00006175778,0.002974888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006236252,"about_ca_system_score_gemma":0.000003285804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005338062,"about_ca_topic_score_gemma":0.00001754633,"domain_scores_codex":[0.9995725,0.00000752241,0.0001296855,0.00008586769,0.0000658299,0.0001385836],"domain_scores_gemma":[0.999844,0.00001030422,0.00001107986,0.00006495235,0.00002421622,0.00004540457],"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":[0.000002847973,0.00004728944,0.00005755651,0.00004858038,0.0001640021,0.000005029459,0.0001312254,0.08218096,0.1308768,0.000009226633,0.001754907,0.7847216],"study_design_scores_gemma":[0.0001114575,0.00000238992,0.00006294166,0.00001991828,0.00003033741,0.000007723092,0.00002420663,0.9942988,0.003608295,0.00008158988,0.001614798,0.0001375081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8985874,0.00008583659,0.03857385,0.00009977743,0.00006838953,0.00005934932,0.000007657238,0.0007544343,0.0617633],"genre_scores_gemma":[0.9831247,0.00003293308,0.01563257,0.0001368123,0.00008198748,0.00000240033,0.00004609852,0.00002186644,0.0009206031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9121179,"threshold_uncertainty_score":0.9978014,"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."}}