{"id":"W2913645927","doi":"10.1007/s10044-019-00790-7","title":"Applying adversarial auto-encoder for estimating human walking gait abnormality index","year":2019,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Abnormality; Point cloud; Point (geometry); Gait; Perspective (graphical); Adversarial system; Pattern recognition (psychology); Index (typography)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004140638,0.0005422782,0.0004168317,0.0004771176,0.0001154227,0.0002770767,0.000377901,0.0004912527,0.0008559961],"category_scores_gemma":[0.001114182,0.0002005241,0.0003412687,0.0003433468,0.0001948527,0.0003219064,0.0003903634,0.0005075025,0.0003956834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002200071,"about_ca_system_score_gemma":0.0002853792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003886628,"about_ca_topic_score_gemma":0.004341003,"domain_scores_codex":[0.9998223,0.00003284491,0.000009266542,0.000052471,0.00005412443,0.00002902478],"domain_scores_gemma":[0.9997258,0.0001112305,0.00002786357,0.00004378813,0.00007648119,0.00001485896],"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":[0.000532472,0.0002089885,0.009065717,0.00009296085,0.0001362297,0.0004310108,0.00005433529,0.5265023,0.03168786,0.002016908,0.003834204,0.425437],"study_design_scores_gemma":[0.000001834746,0.00002294979,0.002087106,0.000003476886,0.000008093374,0.00006416589,0.000003503033,0.9950538,0.002343331,0.000250424,0.0001574838,0.000003741362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1725527,0.000572207,0.8231845,0.0001887648,0.0001933887,0.00004788047,0.0003698024,0.001194559,0.001696145],"genre_scores_gemma":[0.9419329,0.000340653,0.05307707,0.0001042304,0.00005902075,0.00002569228,0.0006077212,0.00006368368,0.003788916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003886628,"threshold_uncertainty_score":0.00772804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151257743378788,"score_gpt":0.2560082191448838,"score_spread":0.2444956417110959,"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."}}