{"id":"W2946997689","doi":"10.1186/s13640-019-0466-z","title":"Estimation of gait normality index based on point clouds through deep auto-encoder","year":2019,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Artificial intelligence; Silhouette; Gait; Computer science; Point cloud; Biometrics; Histogram; Pattern recognition (psychology); Convolutional neural network; Normality; Computer vision; Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525298,0.0008112616,0.0007426097,0.001617147,0.0001459651,0.0004788654,0.0005502827,0.0004054843,0.0008462403],"category_scores_gemma":[0.001074923,0.0003183951,0.000378884,0.00119724,0.0002558225,0.0006239414,0.0005510251,0.0004734462,0.0005631936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003154065,"about_ca_system_score_gemma":0.0004817163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005536869,"about_ca_topic_score_gemma":0.007722497,"domain_scores_codex":[0.999727,0.00002510188,0.00001665372,0.00007603229,0.0001144517,0.00004082909],"domain_scores_gemma":[0.9996001,0.00007476782,0.00006991976,0.00004478929,0.0001760275,0.00003442147],"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":[0.0007747521,0.0002598978,0.02756185,0.0002323044,0.0001676309,0.0003698764,0.0001062997,0.1133695,0.05138176,0.001729996,0.006058336,0.7979879],"study_design_scores_gemma":[0.00001192605,0.00008195604,0.0159075,0.0000239517,0.00002272874,0.0002690738,0.00003164222,0.971798,0.01009838,0.001146847,0.0005870492,0.00002093411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1262533,0.0005225328,0.8687705,0.00007578734,0.00009319265,0.00008492703,0.001036259,0.002070862,0.001092654],"genre_scores_gemma":[0.8392785,0.0005967342,0.1558495,0.00005656065,0.00005590468,0.00009316119,0.002377447,0.00009655843,0.001595554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005536869,"threshold_uncertainty_score":0.01100928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01018046575577146,"score_gpt":0.2525939455735615,"score_spread":0.24241347981779,"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."}}