{"id":"W2036123359","doi":"10.1088/0967-3334/34/8/n63","title":"Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics","year":2013,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":320,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston General Hospital; Toronto Rehabilitation Institute; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Motion capture; Joint (building); Range of motion; Computer science; Motion analysis; Angular velocity; Correlation coefficient; Knee Joint; Simulation; Motion (physics); Artificial intelligence; Physics; Medicine; Engineering; Structural engineering; Surgery","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001932243,0.0002389414,0.0006511107,0.00006824812,0.0001477063,0.000005093999,0.0001971205,0.0001917099,0.0003424285],"category_scores_gemma":[0.0005749697,0.0001695044,0.0002406767,0.0001609686,0.0001075527,0.00008900005,0.0001182464,0.0003301568,0.0002041711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002560631,"about_ca_system_score_gemma":0.0001138797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008852735,"about_ca_topic_score_gemma":0.000007051542,"domain_scores_codex":[0.9958303,0.0007877116,0.001265742,0.0003257363,0.001352128,0.0004384146],"domain_scores_gemma":[0.996613,0.00006803331,0.0008379261,0.0004305596,0.00191324,0.0001372697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003408397,0.00166994,0.0004682756,0.0007683138,0.00009336862,3.450859e-7,0.0001792551,0.00001514265,0.9901984,0.0007085948,0.00424379,0.001620431],"study_design_scores_gemma":[0.00340914,0.002732979,0.9407258,0.002597723,0.000240908,8.23371e-7,0.000964003,0.0008136921,0.03722989,0.009656428,0.001002368,0.0006262583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917033,0.0004553693,0.002348737,0.0003041116,0.001067538,0.002206879,0.00001642249,0.00006912577,0.001828477],"genre_scores_gemma":[0.9987652,0.00009402584,0.0004938993,0.0001148591,0.0001827801,0.0002756902,0.00002325273,0.00001589649,0.00003444078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9529685,"threshold_uncertainty_score":0.691219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1764246707741733,"score_gpt":0.3566951916397466,"score_spread":0.1802705208655733,"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."}}