{"id":"W2779726110","doi":"10.1017/s0263574717000571","title":"A comparative study of in-field motion capture approaches for body kinematics measurement in construction","year":2017,"lang":"en","type":"article","venue":"Robotica","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Hong Kong Polytechnic University; National Science Foundation","keywords":"Kinematics; Motion capture; Computer vision; Computer science; Artificial intelligence; Motion (physics); Angular velocity; Encoder; Trajectory; Field (mathematics); Measure (data warehouse); Simulation; Physics; Mathematics; Data mining","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":[0.002656615,0.0006511878,0.0005446258,0.002140574,0.0003452942,0.0008133597,0.0006073847,0.0007305973,0.001386955],"category_scores_gemma":[0.00608951,0.000333535,0.0005354919,0.0009999112,0.0003467141,0.0007519328,0.0007083478,0.0003219604,0.0003121828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004478165,"about_ca_system_score_gemma":0.000451466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002075242,"about_ca_topic_score_gemma":0.003116393,"domain_scores_codex":[0.997666,0.0008100051,0.0001683877,0.0002944953,0.0008775283,0.0001836138],"domain_scores_gemma":[0.9931678,0.002707818,0.000551764,0.0004049304,0.002935831,0.0002318934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.009651214,0.003299876,0.2213033,0.004246947,0.001263315,0.0005951435,0.005510477,0.01265896,0.1943642,0.001424975,0.002010658,0.543671],"study_design_scores_gemma":[0.0003132175,0.0152402,0.8421538,0.0004654712,0.001114016,0.001557896,0.004629035,0.05741911,0.06854793,0.000470107,0.007847385,0.0002417942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507018,0.001199254,0.04416201,0.00007334356,0.00009859276,0.0002721886,0.0002812903,0.00009672943,0.003114692],"genre_scores_gemma":[0.9774139,0.0008498087,0.01980771,0.00006594221,0.00004453809,0.0001855156,0.0004013377,0.00002485745,0.001206456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002656615,"threshold_uncertainty_score":0.01404965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240711373375331,"score_gpt":0.3490947350918598,"score_spread":0.2250235977543268,"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."}}