{"id":"W2093023668","doi":"10.1109/iembs.2011.6091272","title":"Depth energy image for gait symmetry quantification","year":2011,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université de Montréal","funders":"","keywords":"Gait; Symmetry (geometry); Computer vision; Computer science; Artificial intelligence; Asymmetry; Energy (signal processing); Gait analysis; Image (mathematics); Motion (physics); Mathematics; Physical medicine and rehabilitation; Physics; Geometry; Statistics; 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":[0.0003749992,0.0005234523,0.0003783206,0.001460552,0.0001828709,0.0004647459,0.0004427023,0.0004108264,0.00369864],"category_scores_gemma":[0.001230543,0.0002744722,0.0002309366,0.0005963848,0.0003113294,0.0009562109,0.0006321384,0.0004968134,0.0006826138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003101483,"about_ca_system_score_gemma":0.0002978525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007936841,"about_ca_topic_score_gemma":0.00114225,"domain_scores_codex":[0.9996464,0.00007526694,0.00001654638,0.0000483236,0.000185974,0.00002743378],"domain_scores_gemma":[0.9995863,0.0001585665,0.00005546645,0.00004259717,0.0001278227,0.00002923289],"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":[0.0005795722,0.00009305082,0.003515766,0.0007032497,0.00007679442,0.0002010224,0.0001499564,0.006974913,0.4194486,0.009640341,0.002839105,0.5557777],"study_design_scores_gemma":[0.0001814219,0.001035718,0.04332638,0.0002848185,0.0002282613,0.004622924,0.0003099115,0.417303,0.4822995,0.01494728,0.0350817,0.0003791209],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05091698,0.001611966,0.94183,0.0001317836,0.0001060772,0.0001623512,0.0003949776,0.0004778593,0.00436798],"genre_scores_gemma":[0.3701292,0.001728706,0.6243659,0.0001235449,0.00008896813,0.0002256367,0.0003964721,0.0001538145,0.002787706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00369864,"threshold_uncertainty_score":0.01237321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03468161314916775,"score_gpt":0.2248790544556944,"score_spread":0.1901974413065267,"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."}}