{"id":"W4410007103","doi":"10.2139/ssrn.5229551","title":"A Low-Cost Markerless Motion Capture System to Automate Functional Gait Assessment: Feasibility Study","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Motion capture; Gait; Computer science; Motion (physics); Computer vision; Artificial intelligence; Gait analysis; Physical medicine and rehabilitation; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001726552,0.0007098615,0.0006208852,0.0007534713,0.0002392845,0.0004811576,0.001171473,0.0014062,0.004284048],"category_scores_gemma":[0.002545667,0.0003617992,0.0003233918,0.0003564157,0.0004563087,0.0008300929,0.0006710418,0.0003934066,0.001451872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002419651,"about_ca_system_score_gemma":0.0007517751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001216551,"about_ca_topic_score_gemma":0.001477026,"domain_scores_codex":[0.9988862,0.0003665122,0.0000793166,0.0002016941,0.0003660658,0.0001003408],"domain_scores_gemma":[0.9985313,0.0004802592,0.00008218206,0.0001684669,0.0006175811,0.000120178],"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":[0.008022167,0.004060324,0.04620004,0.00123937,0.0002474065,0.001321471,0.0005527317,0.002004308,0.6266632,0.000763159,0.003372542,0.3055532],"study_design_scores_gemma":[0.003655424,0.1217473,0.398172,0.0002951321,0.00173102,0.0169877,0.001242448,0.118812,0.3225802,0.001027741,0.0133829,0.0003662212],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.763831,0.000685008,0.2295013,0.000344155,0.0001623235,0.002022812,0.001081194,0.0007005704,0.001671583],"genre_scores_gemma":[0.8709038,0.0004024134,0.122894,0.0004427042,0.0001078419,0.001152626,0.001072305,0.00007717397,0.002947184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004284048,"threshold_uncertainty_score":0.01433158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335855842922979,"score_gpt":0.266488827823772,"score_spread":0.2531302693945423,"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."}}