{"id":"W4415412223","doi":"10.1007/978-3-032-03527-1_30","title":"AI-Driven 3D Motion Capture and Human Body Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Motion capture; Inertial measurement unit; Automatic identification and data capture; Match moving; Motion (physics); Virtual reality; Pose; Tracking (education); Analytics; Tracking system","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":[0.0002566847,0.0006897358,0.0006250466,0.0008246365,0.0002734684,0.0008090852,0.001279171,0.0008550354,0.009374297],"category_scores_gemma":[0.0006264141,0.0006954614,0.0005287462,0.001126861,0.0004385651,0.0006410189,0.0008213628,0.0006510157,0.005225484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000503373,"about_ca_system_score_gemma":0.0005299195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005732241,"about_ca_topic_score_gemma":0.01130684,"domain_scores_codex":[0.9997059,0.00002507986,0.000008718302,0.00007287503,0.0001605606,0.00002690607],"domain_scores_gemma":[0.9997414,0.00007372445,0.00001860427,0.00005090148,0.00009912188,0.00001624174],"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":[0.0002576988,0.000109953,0.0009697608,0.0003264017,0.00009619712,0.0001626171,0.00009119746,0.09607492,0.210054,0.009574309,0.02606016,0.6562228],"study_design_scores_gemma":[0.00001300401,0.00007022116,0.002563421,0.00002975447,0.0000168058,0.0003151019,0.00003064895,0.9214392,0.04622575,0.00794012,0.02131063,0.00004532582],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00413775,0.0004651714,0.985006,0.0001328079,0.0001326916,0.00008426343,0.0007136666,0.002746726,0.006580843],"genre_scores_gemma":[0.2329349,0.001406108,0.7271895,0.0005335548,0.0001567325,0.000315583,0.003728455,0.0006757106,0.03305949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009374297,"threshold_uncertainty_score":0.03136015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001590601267716,"score_gpt":0.2245638449674709,"score_spread":0.2145479389547937,"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."}}