{"id":"W2962678776","doi":"10.1109/ccece.2016.7726814","title":"Novel velocity model to improve indoor localization using inertial navigation with sensors on a smartphone","year":2016,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); GNSS applications; Computer science; Inertial navigation system; Heading (navigation); Inertial frame of reference; Gaussian; Computer vision; Artificial intelligence; Inertial measurement unit; Variance (accounting); Algorithm; Simulation; Control theory (sociology); Engineering; Global Positioning System; Physics; Telecommunications","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":[0.000234195,0.000724572,0.0005314925,0.000522969,0.0001988283,0.0004475208,0.0007957603,0.000504668,0.0008616935],"category_scores_gemma":[0.0009316151,0.00024855,0.0004839851,0.0005063504,0.0002017957,0.0008592379,0.0004656204,0.0004498207,0.0005175896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003001484,"about_ca_system_score_gemma":0.0006831957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009445712,"about_ca_topic_score_gemma":0.008030285,"domain_scores_codex":[0.9997182,0.00004410346,0.00001912903,0.00007587009,0.0001109918,0.0000315886],"domain_scores_gemma":[0.9997168,0.00005189524,0.00003736694,0.00004061353,0.0001389526,0.00001424949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001808298,0.00009027866,0.00597613,0.0003345531,0.00008697726,0.0003027347,0.0002193373,0.4770746,0.05143883,0.01022754,0.005035646,0.4490326],"study_design_scores_gemma":[0.00001591625,0.0001044883,0.001228482,0.00001547597,0.00002443959,0.0001424557,0.00002072878,0.9877394,0.005883308,0.0007931085,0.004008939,0.00002323898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01227979,0.0002608162,0.9855871,0.00006551333,0.0001127832,0.00002286072,0.00004492388,0.0008633274,0.0007629063],"genre_scores_gemma":[0.6548073,0.0009240282,0.338825,0.0001005719,0.0001329754,0.0001251355,0.0004162366,0.0002044409,0.004464335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009445712,"threshold_uncertainty_score":0.01878148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558554343105703,"score_gpt":0.2229400334785028,"score_spread":0.2073544900474458,"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."}}