{"id":"W2511570463","doi":"10.1016/j.gaitpost.2016.08.022","title":"Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data","year":2016,"lang":"en","type":"article","venue":"Gait & Posture","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Association Nationale de la Recherche et de la Technologie; Wellcome Trust","keywords":"Gait; Asymmetry; Computer vision; Computer science; Artificial intelligence; Motion capture; Treadmill; Sensitivity (control systems); STRIDE; Mathematics; Physical medicine and rehabilitation; Motion (physics); Medicine; Engineering; Physics; Physical therapy","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":[0.01840451,0.0008935917,0.0008980578,0.003116017,0.0005509148,0.002326368,0.001209902,0.001618334,0.001452612],"category_scores_gemma":[0.08124569,0.0007802615,0.002169884,0.001119589,0.00152834,0.00233183,0.002047392,0.001048427,0.0009100626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006724698,"about_ca_system_score_gemma":0.000909838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003763078,"about_ca_topic_score_gemma":0.004283733,"domain_scores_codex":[0.9873834,0.003746575,0.001791047,0.002384954,0.004078375,0.0006157378],"domain_scores_gemma":[0.9192103,0.05468209,0.008496813,0.005304007,0.01108855,0.00121827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006253818,0.0001089012,0.9878303,0.00005339178,0.0005749149,0.00004271037,0.0002849995,0.00102607,0.00115687,0.0002090287,0.0002003482,0.007887041],"study_design_scores_gemma":[0.00005851014,0.0005598857,0.977038,0.0000939445,0.0004057936,0.0003702048,0.0005185368,0.01707469,0.002064622,0.0007689462,0.0009831212,0.00006363699],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888077,0.0007660039,0.004936759,0.0001247619,0.0002028561,0.0001262181,0.0006400215,0.00005052926,0.004345077],"genre_scores_gemma":[0.9978603,0.0001140271,0.001124587,0.00004906338,0.00003698097,0.00004582421,0.0003548965,0.00001825931,0.0003960665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01840451,"threshold_uncertainty_score":0.09733355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03944757066768254,"score_gpt":0.2563842873455068,"score_spread":0.2169367166778242,"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."}}