{"id":"W3093669302","doi":"10.1109/tim.2020.3033077","title":"Adaptive Gain Regulation of Sensor Fusion Algorithms for Orientation Estimation with Magnetic and Inertial Measurement Units","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Science and Engineering Research Council; Alberta Innovates","keywords":"Sensor fusion; Inertial frame of reference; Orientation (vector space); Fusion; Computer science; Algorithm; Control theory (sociology); Artificial intelligence; Physics; Mathematics","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":[0.002357348,0.0008572895,0.0007385144,0.0005569062,0.0004360157,0.0007282263,0.0008225791,0.0008133829,0.0007196753],"category_scores_gemma":[0.004795834,0.0003568653,0.0006544642,0.0004656398,0.0008021577,0.001084707,0.0008981177,0.001136987,0.0002383164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007707925,"about_ca_system_score_gemma":0.0007600616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003451551,"about_ca_topic_score_gemma":0.002004524,"domain_scores_codex":[0.9987406,0.0003291319,0.00009310136,0.0003339864,0.0003902926,0.0001128986],"domain_scores_gemma":[0.9989477,0.0005135828,0.0001539253,0.00007968193,0.0002813886,0.00002373867],"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":[0.0003145089,0.0001281535,0.001410425,0.000161672,0.0001172315,0.00005848954,0.0003192556,0.5987272,0.02944184,0.01861399,0.0007715365,0.3499357],"study_design_scores_gemma":[0.00001431807,0.00007497375,0.0003358777,0.000009579924,0.00001476447,0.00001561989,0.00001036784,0.9929076,0.003688844,0.002379456,0.0005360867,0.00001260478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01060018,0.0001614777,0.9884615,0.00004241697,0.00001985461,0.00002318462,0.000004392908,0.0001508997,0.0005361692],"genre_scores_gemma":[0.7654232,0.0002341678,0.2329728,0.00008332322,0.00004680704,0.0001683626,0.00003469955,0.0000384564,0.0009982617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003451551,"threshold_uncertainty_score":0.01246703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04973742331839865,"score_gpt":0.2280050617626744,"score_spread":0.1782676384442757,"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."}}