{"id":"W2043038564","doi":"10.1109/ipta.2014.7001958","title":"Video-based analysis of gait with side views","year":2014,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Computer science; Gait; Artificial intelligence; Segmentation; Gait analysis; Ground truth; Human skeleton; Motion capture; Monocular; Motion analysis; Image segmentation; Motion (physics); 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":[],"category_scores_codex":[0.0000864766,0.00007553939,0.0002481251,0.0002936862,0.00001113423,0.000009729381,0.00005219589,0.00002329774,0.001125516],"category_scores_gemma":[0.00001025451,0.00005492612,0.0001433904,0.0008089056,0.00001358022,0.00002869081,0.000002861712,0.0000340321,0.00004715677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007409064,"about_ca_system_score_gemma":0.000003319466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004461674,"about_ca_topic_score_gemma":0.000752088,"domain_scores_codex":[0.9995556,0.00001544539,0.0001564014,0.00008395218,0.0001003147,0.0000882223],"domain_scores_gemma":[0.9996864,0.00004235227,0.00002344031,0.0001606105,0.00004063436,0.00004660496],"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":[0.00001106948,0.00007357774,0.0292456,0.0001055166,0.003323728,0.000001816625,0.00007889999,0.9233655,0.009669396,0.0005040527,0.001497511,0.03212333],"study_design_scores_gemma":[0.0002561058,0.00002342034,0.01433493,0.00001206735,0.00142587,1.644087e-7,0.00002487192,0.9661301,0.01143534,0.00002252857,0.006189377,0.0001452565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3628566,0.00003547084,0.4981731,0.00008142669,0.00002016877,0.00005356604,0.000007382579,0.0002497303,0.1385226],"genre_scores_gemma":[0.9963954,0.000003841767,0.003116025,0.0001571746,0.000009041998,0.000004408832,0.00002428537,0.000008766335,0.0002810218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6335388,"threshold_uncertainty_score":0.9997876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090298923089144,"score_gpt":0.2055698005764839,"score_spread":0.1946668113455924,"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."}}