{"id":"W2951467890","doi":"10.1109/jbhi.2019.2923209","title":"Vision-Based Freezing of Gait Detection With Anatomic Directed Graph Representation","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; National Health and Medical Research Council; Parkinson Canada; University of Sydney","keywords":"Computer science; Artificial intelligence; Graph; Context (archaeology); Feature learning; Convolutional neural network; Deep learning; Machine learning; Computer vision; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003541375,0.00006593089,0.0002418063,0.000377525,0.00003033515,0.00001399023,0.00004337708,0.00005014984,0.00002720584],"category_scores_gemma":[0.00001149071,0.00004650907,0.00004980463,0.0003519357,0.00003758433,0.0001588333,0.00000280261,0.0001595339,0.000003041749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003152911,"about_ca_system_score_gemma":0.00005533648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001651822,"about_ca_topic_score_gemma":0.00001044182,"domain_scores_codex":[0.9988316,0.00001596209,0.0007352994,0.00002673293,0.0002830516,0.0001073876],"domain_scores_gemma":[0.9992718,0.000046079,0.0003626806,0.00005418331,0.0001096453,0.000155657],"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":[0.0003274838,0.0003362379,0.01654421,0.007263937,0.0006702258,0.00001189111,0.00443312,0.02664716,0.01912068,0.00001971299,0.004185773,0.9204396],"study_design_scores_gemma":[0.003473811,0.001449167,0.01741511,0.001157658,0.00008575098,0.00009990259,0.001324385,0.9618536,0.01113374,0.0001520228,0.001636358,0.0002185449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9380444,0.00009594831,0.06124972,0.0001802245,0.0002146282,0.0000720134,0.000005446105,0.00002854985,0.0001090564],"genre_scores_gemma":[0.9965541,0.0002587639,0.00299073,0.0001450817,0.00003793585,5.111043e-7,0.000005015696,0.000005177564,0.000002649585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9352064,"threshold_uncertainty_score":0.1896585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137344938942956,"score_gpt":0.2653487971855819,"score_spread":0.2539753477961523,"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."}}