{"id":"W2048978981","doi":"10.1109/iembs.2011.6091257","title":"Contactless abnormal gait detection","year":2011,"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":"","keywords":"Artificial intelligence; Gait; Computer vision; Computer science; Outlier; Movement (music); Process (computing); Line (geometry); Gait analysis; Symmetry (geometry); Pattern recognition (psychology); Mathematics; Physical medicine and rehabilitation; Acoustics; Physics; Geometry; Medicine","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":[0.0002201088,0.0005024571,0.0005771564,0.002016229,0.0001857806,0.0004060173,0.0007962059,0.0005329291,0.001664421],"category_scores_gemma":[0.001057256,0.0002333108,0.000277872,0.0007423685,0.0002620266,0.0005686168,0.0004184764,0.0003053559,0.0008472819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001663921,"about_ca_system_score_gemma":0.0002373149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008118303,"about_ca_topic_score_gemma":0.001283911,"domain_scores_codex":[0.9995227,0.00004967323,0.00003403095,0.00008954621,0.0002644742,0.00003947478],"domain_scores_gemma":[0.9992822,0.0001190249,0.0001395145,0.0001048624,0.0002952983,0.00005916689],"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":[0.0004983594,0.0001722913,0.01461152,0.0003456,0.00009100266,0.001158865,0.0001267393,0.004012673,0.2123146,0.001448438,0.005028051,0.760192],"study_design_scores_gemma":[0.0001232501,0.001030969,0.06494431,0.00008154683,0.0001320451,0.01567361,0.000150646,0.6826385,0.2148451,0.00259778,0.01764044,0.0001418144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1533993,0.0009176631,0.8390582,0.0001463586,0.0002179292,0.0001569269,0.0005040372,0.00281944,0.002780313],"genre_scores_gemma":[0.7243378,0.000518771,0.2700957,0.0001082601,0.0001300137,0.0001000805,0.0007884186,0.0001399684,0.003780966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002016229,"threshold_uncertainty_score":0.005568087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780397168005188,"score_gpt":0.1757855053975194,"score_spread":0.1579815337174675,"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."}}