{"id":"W4400579002","doi":"10.1109/jsen.2024.3423374","title":"Enhancing Automatic Inertial Sensor Calibration Algorithm for Accurate Joint Angle Estimation in High Flexion Postures","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Computer science; Inertial measurement unit; Joint (building); Computer vision; Inertial frame of reference; Artificial intelligence; Accelerometer; Algorithm; Engineering; Mathematics; Physics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001066474,0.0007623745,0.0005951798,0.000803658,0.0002847844,0.0006371496,0.0006733098,0.0005560388,0.001810909],"category_scores_gemma":[0.004380076,0.0003181973,0.0003665939,0.0006950658,0.0002267725,0.0006252791,0.00062455,0.0004870737,0.001303895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001573951,"about_ca_system_score_gemma":0.0006239774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376324,"about_ca_topic_score_gemma":0.002667684,"domain_scores_codex":[0.9990214,0.0002528559,0.00006817308,0.000214585,0.0003788421,0.00006419996],"domain_scores_gemma":[0.9984652,0.0003798408,0.0002019289,0.0001535698,0.000766526,0.00003287367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004904233,0.0002157105,0.01823035,0.0003631841,0.00009295164,0.0001288534,0.0004703595,0.02796286,0.1132496,0.00187714,0.00321804,0.8337005],"study_design_scores_gemma":[0.0001106524,0.0007091003,0.07245803,0.0001066414,0.00008898611,0.0008185321,0.0003005926,0.8194527,0.08903722,0.002154314,0.01464898,0.0001141895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05234117,0.0002904099,0.9435552,0.0000767918,0.00008800354,0.00009634523,0.00009797695,0.001582666,0.001871424],"genre_scores_gemma":[0.5143316,0.0003092773,0.4823596,0.0001132769,0.00006227835,0.0002965307,0.0003647602,0.0002069418,0.001955766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001810909,"threshold_uncertainty_score":0.006058097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02725394698906624,"score_gpt":0.3269347476115659,"score_spread":0.2996808006224997,"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."}}