{"id":"W4396702975","doi":"10.1016/j.measurement.2024.114857","title":"Improved markerless gait kinematics measurement using a biomechanically-aware algorithm with subject-specific geometric modeling","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"HORIZON EUROPE Marie Sklodowska-Curie Actions; H2020 Marie Skłodowska-Curie Actions; European Commission","keywords":"Kinematics; Mean squared error; Gait; Computer science; Artificial intelligence; Computer vision; Smoothing; Gait analysis; Algorithm; RGB color model; Mathematics; Statistics; Physical medicine and rehabilitation","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003159269,0.0005421504,0.0006721955,0.0009916143,0.0001524826,0.000313843,0.0002448298,0.0001228888,0.0002122173],"category_scores_gemma":[0.00004300617,0.000420047,0.0002336227,0.001711156,0.00004657421,0.0001639331,0.000145803,0.0003588002,0.00004209473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001894787,"about_ca_system_score_gemma":0.0004608802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007095849,"about_ca_topic_score_gemma":0.00002251994,"domain_scores_codex":[0.9939427,0.00008705498,0.0007860006,0.0008825324,0.003500128,0.0008015566],"domain_scores_gemma":[0.9977636,0.0000237735,0.0001156222,0.0007475313,0.001020554,0.000328964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001155325,0.003967287,0.0002014886,0.01167253,0.007187311,0.001001045,0.0008531778,0.002988586,0.1436903,0.0008944683,0.006455497,0.8199329],"study_design_scores_gemma":[0.003016351,0.0009530031,0.0001010435,0.003524153,0.001373209,0.0000530773,0.0004954993,0.97824,0.004815881,0.0001778327,0.006458405,0.0007915067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0104275,0.00496269,0.9790444,0.0006745007,0.0008918692,0.00216439,0.000008562991,0.0004195847,0.001406442],"genre_scores_gemma":[0.9486016,0.0001881585,0.05017868,0.0001712914,0.0003160817,0.000196496,0.00002030379,0.0001416543,0.0001857423],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9752514,"threshold_uncertainty_score":0.9998251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08179102494633383,"score_gpt":0.2800652785399376,"score_spread":0.1982742535936037,"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."}}