{"id":"W2986111177","doi":"10.1109/tnsre.2019.2953707","title":"Multivariate Analysis of Joint Motion Data by Kinect: Application to Parkinson’s Disease","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dr. Georges-L.-Dumont University Hospital Centre; McGill University; Montreal Neurological Institute and Hospital","funders":"University of Electronic Science and Technology of China; National Natural Science Foundation of China","keywords":"Multivariate statistics; Bivariate analysis; Motion (physics); Multivariate analysis; Joint (building); Canonical correlation; Receiver operating characteristic; Trunk; Correlation; Artificial intelligence; Feature (linguistics); Multivariate normal distribution; Computer science; Pattern recognition (psychology); Mathematics; Physical medicine and rehabilitation; Psychology; Statistics; Medicine; Engineering; Biology; Geometry","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":[0.0006412422,0.0005939705,0.0006627571,0.001333444,0.000154949,0.0003698216,0.0002175616,0.0003174156,0.0005517611],"category_scores_gemma":[0.001615857,0.000185135,0.0004618356,0.001467624,0.0001663465,0.0002759979,0.0004696061,0.0003159172,0.0001204574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001417694,"about_ca_system_score_gemma":0.000326555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003589299,"about_ca_topic_score_gemma":0.004314344,"domain_scores_codex":[0.9996485,0.0001062001,0.00003721694,0.00007897106,0.0001029288,0.00002623345],"domain_scores_gemma":[0.9996159,0.0001450858,0.00006763286,0.00003293573,0.000102947,0.00003539782],"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":[0.001783269,0.0004388623,0.1006646,0.00116517,0.0004415606,0.001253018,0.0006346002,0.05168397,0.1346374,0.001886562,0.003755503,0.7016556],"study_design_scores_gemma":[0.0000785325,0.000404469,0.2949858,0.0001141357,0.0002161118,0.001478899,0.0003728936,0.6681299,0.02799433,0.00231559,0.003756757,0.0001527058],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6221167,0.003745235,0.3674502,0.0003256352,0.0001267157,0.000200196,0.003011116,0.001432303,0.001591978],"genre_scores_gemma":[0.8968551,0.001148674,0.1004011,0.00004701714,0.00004107241,0.0001060214,0.0008638203,0.0000612994,0.0004758859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003589299,"threshold_uncertainty_score":0.007136822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00951934256279126,"score_gpt":0.2171048585941288,"score_spread":0.2075855160313375,"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."}}