{"id":"W2798029488","doi":"10.1109/bhi.2018.8333364","title":"Assessment of gait normality using a depth camera and mirrors","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Normality; Computer vision; Artificial intelligence; Gait; Feature (linguistics); Computer science; Sequence (biology); Sliding window protocol; Motion (physics); Window (computing); Gait analysis; Pattern recognition (psychology); Mathematics; Physical medicine and rehabilitation; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000132152,0.0001577389,0.0003072781,0.0001237235,0.00002081118,0.00002982016,0.00007415334,0.0001268995,0.0005151147],"category_scores_gemma":[0.000005378932,0.0001497462,0.0001069635,0.000074085,0.00004204189,0.00003380684,0.0001314102,0.0001904625,0.000004196257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004488691,"about_ca_system_score_gemma":0.00002963982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006249395,"about_ca_topic_score_gemma":0.0002652845,"domain_scores_codex":[0.9992884,0.00002248285,0.0002642685,0.0001747602,0.0001276606,0.0001224595],"domain_scores_gemma":[0.9995848,0.00001387236,0.00006189452,0.0001959007,0.00007845425,0.00006505469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001851986,0.0006209181,0.7528848,0.01448122,0.007759681,0.00004548211,0.001720223,0.1248012,0.04389989,0.001008413,0.002713129,0.05004647],"study_design_scores_gemma":[0.0002239215,0.00001350921,0.1596566,0.0002011567,0.0003198232,0.000005554226,0.0001390603,0.8356218,0.002762172,0.0003525656,0.0002822031,0.00042166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9345536,0.00006469995,0.03661713,0.00000940201,0.0001436156,0.00009010106,0.00002812868,0.00009902789,0.02839436],"genre_scores_gemma":[0.9744419,0.00008296213,0.02530366,0.00002295004,0.00003016995,0.000004999752,0.00003008274,0.00001631704,0.00006693636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7108206,"threshold_uncertainty_score":0.6106472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0586084570728042,"score_gpt":0.325918357279446,"score_spread":0.2673099002066418,"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."}}