{"id":"W2548179557","doi":"10.1049/iet-bmt.2015.0072","title":"Human gait recognition from motion capture data in signature poses","year":2016,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Masarykova Univerzita; National Science Foundation","keywords":"Computer science; Gait; Artificial intelligence; Classifier (UML); Biometrics; Gait analysis; Feature (linguistics); Pattern recognition (psychology); Computer vision; Motion capture; Motion (physics); Physical medicine and rehabilitation","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":[0.0001461648,0.0001289624,0.0001525884,0.001330054,0.00002886846,0.00004545922,0.0002345942,0.0001924661,0.0005876539],"category_scores_gemma":[0.0001674493,0.0001006277,0.00004385035,0.002650108,0.00001755553,0.000297992,0.0000448167,0.000134836,0.0002485495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006730964,"about_ca_system_score_gemma":0.000004886542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001277287,"about_ca_topic_score_gemma":0.0001628002,"domain_scores_codex":[0.9991381,0.00002689457,0.0002110205,0.000254789,0.000193989,0.0001752284],"domain_scores_gemma":[0.9994258,0.00008021736,0.00003735276,0.0003366278,0.00005623174,0.00006380552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006696662,0.0001971664,0.01000526,0.00006763916,0.0001634573,0.00004073543,0.00012727,0.00006560827,0.1728744,0.00001744362,0.0227527,0.7936816],"study_design_scores_gemma":[0.01405902,0.0002725127,0.6402697,0.001994434,0.001192392,0.00003315893,0.001450387,0.03576084,0.1428275,0.02650873,0.1291506,0.006480639],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9698809,0.001547021,0.0203441,0.0003714484,0.0005907452,0.0001742863,0.003534463,0.0004904126,0.00306666],"genre_scores_gemma":[0.9968392,0.0002476338,0.0007321645,0.00006931872,0.0001379881,0.000003997498,0.001811634,0.00002250859,0.0001355245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.787201,"threshold_uncertainty_score":0.6434398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0458078803232287,"score_gpt":0.2541053404414896,"score_spread":0.2082974601182609,"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."}}