{"id":"W2151403658","doi":"10.5539/cis.v8n4p56","title":"Human Identification by Gait Using Time Delay Neural Networks","year":2015,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Zarqa University","keywords":"Acceleration; Gait; Computer science; Artificial intelligence; Biometrics; Computer vision; Identification (biology); Artificial neural network; Viewing angle; Set (abstract data type); Motion (physics); Feature (linguistics); Segmentation; Gait analysis; Pattern recognition (psychology); Physical medicine and rehabilitation; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003436885,0.0003261592,0.0003102154,0.0004930962,0.0001463198,0.0003941085,0.0003052732,0.0003331485,0.001131033],"category_scores_gemma":[0.0009219442,0.000168146,0.0002421492,0.0004938805,0.0001882063,0.0004876897,0.0002125738,0.0003457347,0.0002914679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005068241,"about_ca_system_score_gemma":0.0002292817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006202339,"about_ca_topic_score_gemma":0.005062986,"domain_scores_codex":[0.9998481,0.00002881276,0.00001076168,0.00005419923,0.00003903348,0.00001917949],"domain_scores_gemma":[0.9997742,0.00009499003,0.00002813142,0.00001612292,0.00007690824,0.000009687632],"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":[0.0002985107,0.0001394546,0.002514096,0.00007890406,0.00007821779,0.0001087502,0.0000493329,0.2984484,0.02033106,0.002614559,0.002029381,0.6733093],"study_design_scores_gemma":[0.000002896151,0.00002441355,0.0005487866,0.0000037294,0.000005822033,0.00002033419,0.000004507318,0.9960431,0.002421424,0.00057691,0.0003440099,0.000003994009],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1075656,0.0009457565,0.8845426,0.0002574172,0.0002611614,0.00006437485,0.0001625201,0.001499231,0.004701288],"genre_scores_gemma":[0.8639111,0.000486295,0.1276921,0.000116592,0.00005989515,0.00005179209,0.0002412444,0.00003844932,0.007402435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006202339,"threshold_uncertainty_score":0.0123325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01783700015225926,"score_gpt":0.2368851733683759,"score_spread":0.2190481732161167,"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."}}