{"id":"W4407261680","doi":"10.1186/s12938-025-01347-y","title":"The effect of depth data and upper limb impairment on lightweight monocular RGB human pose estimation models","year":2025,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"University of Toronto","keywords":"Artificial intelligence; Computer vision; Monocular; Computer science; Pose; RGB color model; Estimation; Engineering","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":[0.0003269064,0.000125117,0.0002393938,0.0001806675,0.00004955392,0.00001028082,0.000123482,0.00009299437,0.000005575512],"category_scores_gemma":[0.000394363,0.00007206108,0.00006372971,0.0001754842,0.00008173723,0.00004150731,0.00008536696,0.000149366,0.000001475662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004540565,"about_ca_system_score_gemma":0.00003572353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006974734,"about_ca_topic_score_gemma":7.115016e-7,"domain_scores_codex":[0.9991109,0.00002602249,0.0002763647,0.0002006105,0.0002442461,0.0001418617],"domain_scores_gemma":[0.9989572,0.0004602229,0.00003448482,0.0004176708,0.00002416945,0.0001063142],"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":[0.003296823,0.004985743,0.02592268,0.01152474,0.003369139,0.0001031336,0.000632108,0.01994483,0.1262428,0.01129093,0.03114161,0.7615454],"study_design_scores_gemma":[0.001993817,0.001947857,0.02874394,0.0007247249,0.000134735,0.000007411176,0.00001480553,0.9514424,0.002185342,0.00005422855,0.01265024,0.0001004522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794694,0.00132377,0.01497221,0.003122534,0.0004243235,0.0004826488,0.00005396521,0.00007911176,0.0000721049],"genre_scores_gemma":[0.9928998,0.0001496077,0.006187956,0.00007131985,0.0001347032,0.00001447765,0.0003565544,0.00001455236,0.0001709897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9314976,"threshold_uncertainty_score":0.2938566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005155055122396,"score_gpt":0.2986002307043795,"score_spread":0.2885486801531555,"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."}}