{"id":"W3162079911","doi":"10.1016/j.inffus.2021.04.009","title":"Sensor fusion algorithms for orientation tracking via magnetic and inertial measurement units: An experimental comparison survey","year":2021,"lang":"en","type":"article","venue":"Information Fusion","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Gyroscope; Orientation (vector space); Accelerometer; Computer science; Inertial measurement unit; Sensor fusion; Kalman filter; Algorithm; Particle filter; Magnetometer; Tracking (education); Computer vision; Real-time computing; Artificial intelligence; Mathematics; Engineering; Physics","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":[0.00793492,0.00110979,0.0008557591,0.002453797,0.0005445614,0.002155305,0.001165234,0.00103877,0.001659778],"category_scores_gemma":[0.01457428,0.000402632,0.0007862508,0.002812112,0.0007378932,0.004097794,0.0008120327,0.0009744541,0.0006415473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008202345,"about_ca_system_score_gemma":0.001135616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00338626,"about_ca_topic_score_gemma":0.002032529,"domain_scores_codex":[0.9967572,0.0008608314,0.0003291675,0.0004121341,0.001521858,0.0001188122],"domain_scores_gemma":[0.9913304,0.003853799,0.0006510469,0.0008716949,0.003232741,0.00006035437],"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":[0.0007446265,0.0002886149,0.009769737,0.001143376,0.0004785017,0.00003416152,0.0002036188,0.04582201,0.01098265,0.01405053,0.002583412,0.9138988],"study_design_scores_gemma":[0.0002254483,0.003511198,0.04513611,0.001233679,0.001329381,0.001250937,0.001086966,0.7168448,0.1395583,0.02748642,0.06200517,0.0003314659],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.08781415,0.03640579,0.8562088,0.001068393,0.0005688145,0.0002620883,0.0004499078,0.0009550445,0.01626701],"genre_scores_gemma":[0.7347129,0.03109157,0.2284116,0.0003874741,0.0004222775,0.0002154337,0.001529523,0.0001951916,0.003034069],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00793492,"threshold_uncertainty_score":0.04196435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0496632414804646,"score_gpt":0.2772757418159273,"score_spread":0.2276125003354627,"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."}}