{"id":"W2156499774","doi":"10.1109/jsen.2010.2044238","title":"Synergism of INS and PDR in Self-Contained Pedestrian Tracking With a Miniature Sensor Module","year":2010,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gyroscope; Pedestrian; Dead reckoning; Accelerometer; Tracking (education); Computer science; Inertial measurement unit; Inertial navigation system; Tracking system; Computer vision; Position (finance); Artificial intelligence; Real-time computing; Inertial frame of reference; Engineering; Global Positioning System; Kalman filter; Telecommunications","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":[0.0001532153,0.0001522014,0.0002281057,0.0002765741,0.00005617029,0.00004768826,0.0001029201,0.0002107585,0.00001350822],"category_scores_gemma":[0.0000564002,0.0001235366,0.0000362552,0.0002259524,0.00007121387,0.0001536157,0.000007228312,0.0007521373,0.000001053272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002532756,"about_ca_system_score_gemma":0.00002105232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001345383,"about_ca_topic_score_gemma":0.0002052568,"domain_scores_codex":[0.9992129,0.00002114135,0.0002744024,0.0001080406,0.0001477546,0.0002357448],"domain_scores_gemma":[0.9995981,0.00004239514,0.00007986501,0.0001340946,0.00007576866,0.00006971355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003537383,0.0003346135,0.279173,0.0006730242,0.0005696478,0.001846949,0.0168647,0.2224495,0.4603,0.001102663,0.001256951,0.01507535],"study_design_scores_gemma":[0.00932479,0.0005973694,0.230402,0.0005863935,0.000179331,0.004740402,0.005729931,0.1372021,0.6060219,0.001005817,0.002561415,0.001648625],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998298,0.00005998619,0.0006497091,0.00007237654,0.0003109271,0.00008895829,0.000006114617,0.0001643999,0.0003495219],"genre_scores_gemma":[0.9965241,0.00008408895,0.003208222,0.00001032224,0.0001058932,0.00000149407,7.549895e-7,0.00002921332,0.00003590904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1457219,"threshold_uncertainty_score":0.5037675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004748462968871655,"score_gpt":0.1939587599914745,"score_spread":0.1892102970226028,"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."}}