{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000901453,0.0002727254,0.0003008922,0.0004192203,0.000234557,0.0009277372,0.0003499364,0.00008699158,0.0002705345],"category_scores_gemma":[0.00008078724,0.0002417241,0.00009184399,0.001068987,0.0004791561,0.002459005,0.00009810939,0.0004580221,0.0001031245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001799872,"about_ca_system_score_gemma":0.0001492659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003519308,"about_ca_topic_score_gemma":0.0000365337,"domain_scores_codex":[0.9973601,0.00002744022,0.0004449184,0.0009704018,0.0004575852,0.0007395776],"domain_scores_gemma":[0.9991674,0.00005468787,0.0001266308,0.000399341,0.00009123803,0.0001606629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002077095,0.001789069,0.1506073,0.000483692,0.00006740241,0.0004159057,0.02452533,0.002968006,0.02505431,0.007870466,0.005608012,0.7804028],"study_design_scores_gemma":[0.002995893,0.0007922729,0.6900248,0.005773025,0.0001926823,0.00024145,0.02665698,0.04308026,0.008704089,0.0006659701,0.2171297,0.003742842],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856906,0.00657146,0.001713036,0.0002517087,0.002070582,0.0003235031,0.00001180464,0.0001424215,0.003224868],"genre_scores_gemma":[0.9966303,0.0002705299,0.002351512,0.00002997386,0.0001220465,0.00002830844,0.00002541262,0.0000387738,0.0005032032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.77666,"threshold_uncertainty_score":0.9857222,"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."}}