{"id":"W4391609218","doi":"10.36950/2024.2ciss047","title":"Optimizing wearable motion tracking by assessing sagittal joint angle accuracy with minimal sensor use","year":2024,"lang":"en","type":"article","venue":"Current Issues in Sport Science (CISS)","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wearable computer; Tracking (education); Sagittal plane; Joint (building); Computer science; Computer vision; Match moving; Motion (physics); Artificial intelligence; Motion sensors; Engineering; Medicine; Psychology; Embedded 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":[0.001420263,0.001057777,0.0005605988,0.000616234,0.0002126441,0.0006733641,0.0006211539,0.0006029776,0.0009554679],"category_scores_gemma":[0.00489628,0.0003584607,0.0004729184,0.0004958201,0.0002584343,0.0007888689,0.000493709,0.0001957144,0.0002657621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003224875,"about_ca_system_score_gemma":0.0005443048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002864789,"about_ca_topic_score_gemma":0.003167665,"domain_scores_codex":[0.9990923,0.0002683073,0.00008239638,0.0002303295,0.0002548905,0.00007178287],"domain_scores_gemma":[0.9983711,0.0007741149,0.0003206178,0.0001836276,0.0003145376,0.00003606313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008947906,0.000801867,0.1152492,0.0009254284,0.0003248725,0.0002498232,0.0003824398,0.3508322,0.2429931,0.001002813,0.0007054385,0.2856381],"study_design_scores_gemma":[0.00007289272,0.002667188,0.09462246,0.0001193772,0.00019307,0.0003795305,0.0001609019,0.818048,0.08147928,0.0008555439,0.001338347,0.00006348389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6067562,0.0004073896,0.3904245,0.0001100549,0.00002589855,0.0001783804,0.000198173,0.0005180565,0.001381333],"genre_scores_gemma":[0.9195725,0.0001586102,0.07955523,0.00002539019,0.00001035669,0.0001338761,0.0001220768,0.00002563525,0.0003962856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002864789,"threshold_uncertainty_score":0.007511139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05161554930537343,"score_gpt":0.3701267753454301,"score_spread":0.3185112260400567,"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."}}