{"id":"W4410981915","doi":"10.1007/s10439-025-03762-7","title":"An Augmented Full-Body Model that Improves Upper Body Tracking and Reduces Dynamic Inconsistency in Complex Motion","year":2025,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Tracking (education); Motion (physics); Upper body; Match moving; Motion capture; Computer science; Lower body; Control theory (sociology); Physical medicine and rehabilitation; Computer vision; Artificial intelligence; Medicine; Psychology","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":[0.0006495195,0.0001452935,0.0003013827,0.0003569271,0.0001015127,0.000009057801,0.0001299861,0.0002190354,0.000007181306],"category_scores_gemma":[0.00011207,0.0001386151,0.00005209433,0.0002738794,0.00007105229,0.0002211876,0.00006572537,0.0003436636,0.000001600608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003574852,"about_ca_system_score_gemma":0.00006896554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007517555,"about_ca_topic_score_gemma":0.0000174335,"domain_scores_codex":[0.9985994,0.00009154005,0.0005136767,0.0002544928,0.0001795802,0.0003613384],"domain_scores_gemma":[0.9994298,0.0001052505,0.0001117182,0.0001685759,0.00006443471,0.0001202352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004946057,0.000412182,0.001267834,0.0007782944,0.0000503182,0.00000259961,0.0005957566,0.00009991579,0.9775822,0.001100003,0.0002303357,0.01783113],"study_design_scores_gemma":[0.0006382507,0.0000664021,0.2855386,0.000703274,0.00001316235,7.419064e-7,0.000405087,0.7114612,0.0002106501,0.0007439658,0.0001029101,0.0001156976],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750193,0.0004123834,0.02252829,0.001214431,0.0002407948,0.0003080352,0.00002293409,0.00008400301,0.0001698599],"genre_scores_gemma":[0.9981056,0.0002995932,0.001122276,0.000202771,0.00003680766,0.00003577843,0.0001177766,0.00001432477,0.00006510387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9773715,"threshold_uncertainty_score":0.5652559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0402837712697052,"score_gpt":0.3843857327669527,"score_spread":0.3441019614972475,"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."}}