{"id":"W3168077935","doi":"10.3138/ptc-2020-0051","title":"Evaluating Lower Limb Kinematics Using Microsoft’s Kinect: A Simple, Novel Method","year":2021,"lang":"en","type":"article","venue":"Physiotherapy Canada","topic":"Cerebral Palsy and Movement Disorders","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Microsoft Visual Studio; Computer science; Motion capture; Software; Kinematics; Gait; Mean squared error; Computer vision; Artificial intelligence; Simulation; Motion (physics); Computer graphics (images); Mathematics; Physical medicine and rehabilitation; Medicine; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001526703,0.0002119226,0.0004028457,0.00005013635,0.0001169636,0.00002749158,0.00007768663,0.00004910038,0.001124598],"category_scores_gemma":[0.000101061,0.0002010575,0.0001312464,0.0003827357,0.00002011057,0.00004991216,0.000032397,0.0001479283,0.000001729217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002443515,"about_ca_system_score_gemma":0.001743224,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2587083,"about_ca_topic_score_gemma":0.2282651,"domain_scores_codex":[0.9984766,0.00004582587,0.0003215315,0.00032848,0.0004772449,0.0003502945],"domain_scores_gemma":[0.999036,0.0001268652,0.0001182285,0.0003813104,0.0001992687,0.0001382947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001024639,0.0002081713,0.0003248002,0.0002123397,0.0001839407,0.0000301773,0.0001068036,0.001257524,0.9902151,0.0001112438,0.002386635,0.004860771],"study_design_scores_gemma":[0.02424243,0.001717055,0.01437028,0.00136997,0.0009293391,0.0003708109,0.002737264,0.28127,0.5464534,0.00212653,0.1217041,0.002708734],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691489,0.0007651324,0.02511779,0.002399497,0.0005393086,0.0005419547,0.00005324934,0.00005279646,0.001381344],"genre_scores_gemma":[0.7245174,0.0001243327,0.2187693,0.05146518,0.0007364561,0.00003452383,0.0001818407,0.0001925042,0.003978408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4437617,"threshold_uncertainty_score":0.9997885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05565541396160126,"score_gpt":0.4011131124656732,"score_spread":0.3454576985040719,"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."}}