{"id":"W4312835589","doi":"10.2139/ssrn.4246368","title":"Principal Component Analysis of Whole-Body Kinematics Using Markerless Motion Capture During Static Balance Tasks","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Kinematics; Principal component analysis; Motion capture; Motion (physics); Balance (ability); Motion analysis; Component (thermodynamics); Physical medicine and rehabilitation; Computer science; Artificial intelligence; Physics; Medicine; Classical mechanics","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":[0.0003881253,0.0005281048,0.0003468913,0.0007768036,0.0002540643,0.0005250003,0.0002165624,0.0002614574,0.002204041],"category_scores_gemma":[0.00141165,0.000182267,0.000366212,0.001042706,0.0001214235,0.000304962,0.0003177108,0.0002728655,0.0006609273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001359702,"about_ca_system_score_gemma":0.0005197033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005431042,"about_ca_topic_score_gemma":0.00802711,"domain_scores_codex":[0.9997203,0.00004324441,0.00001928271,0.0000829822,0.00008460847,0.00004949884],"domain_scores_gemma":[0.9997031,0.00007777219,0.00002692684,0.00002380792,0.0001538562,0.00001450345],"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":[0.003318134,0.000486239,0.04268386,0.0006039661,0.0003407979,0.0001472826,0.0008279017,0.00860527,0.2476363,0.000632614,0.005177313,0.6895403],"study_design_scores_gemma":[0.00008243736,0.0006788463,0.9218295,0.00005923725,0.0002295531,0.000342826,0.0002063071,0.05463871,0.01822857,0.0004610065,0.003167645,0.00007547921],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8083894,0.0007903851,0.1805495,0.0001429947,0.0001781944,0.0006067954,0.004138536,0.001187661,0.004016541],"genre_scores_gemma":[0.9511632,0.0004290418,0.04224155,0.00004025688,0.00004957928,0.0004583124,0.00245773,0.0001276226,0.003032808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005431042,"threshold_uncertainty_score":0.01079887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763009483508349,"score_gpt":0.327369002399838,"score_spread":0.3097389075647545,"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."}}