{"id":"W3156454664","doi":"10.1016/j.jbiomech.2021.110414","title":"Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system","year":2021,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":167,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Motion capture; Kinematics; Gait analysis; Treadmill; Artificial intelligence; Effect of gait parameters on energetic cost; Motion analysis; Motion (physics); Computer science; Physical medicine and rehabilitation; Computer vision; Medicine; Physical therapy; Physics","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":[0.001769454,0.0001514034,0.0004855916,0.0002406202,0.0002397829,0.00001669528,0.0001319916,0.0002901668,0.00001718619],"category_scores_gemma":[0.00007752006,0.000138385,0.0002334652,0.000408727,0.00001634216,0.0001586868,0.00004716769,0.0007942035,0.000002498148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005127542,"about_ca_system_score_gemma":0.0006481197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006344048,"about_ca_topic_score_gemma":0.00001475788,"domain_scores_codex":[0.9969153,0.001060841,0.001062283,0.0001679286,0.0005072153,0.0002863793],"domain_scores_gemma":[0.9968066,0.000148405,0.001956975,0.0001780207,0.0007872608,0.0001227608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003904472,0.002663703,0.02297177,0.009707345,0.001059745,0.0007914932,0.002543043,0.001448767,0.6415932,0.00210299,0.0004857344,0.3142417],"study_design_scores_gemma":[0.002320828,0.0001428596,0.008703209,0.002390359,0.0002140127,0.00007418562,0.008606964,0.9763124,0.0005207126,0.0002079811,0.0003073699,0.0001991121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3572517,0.0004002815,0.640327,0.00008404879,0.001580777,0.0002058078,0.00002008976,0.0000203803,0.0001098506],"genre_scores_gemma":[0.9169182,0.00006347078,0.08266905,0.0000663908,0.0001598887,0.000003635834,0.00003581418,0.00002508301,0.00005849928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9748636,"threshold_uncertainty_score":0.5643175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03134871610114701,"score_gpt":0.350807794831322,"score_spread":0.3194590787301749,"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."}}