{"id":"W4256349005","doi":"10.32920/ryerson.14656776.v1","title":"Self-Contained Pedestrian Tracking With Mems Sensors","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gyroscope; Inertial measurement unit; Accelerometer; Computer science; Dead reckoning; Heading (navigation); Tracking (education); Tracking system; Global Positioning System; Kalman filter; Inertial navigation system; Real-time computing; Inertial frame of reference; Computer vision; Engineering; Artificial intelligence; Telecommunications; Aerospace engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002452612,0.0003737532,0.0004192007,0.0005292501,0.0001602379,0.0003344192,0.0003718263,0.0003647722,0.0005880169],"category_scores_gemma":[0.0003484046,0.0002425693,0.0003872712,0.0005250286,0.00009546924,0.0004491441,0.0003682886,0.000173141,0.0003197451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001889105,"about_ca_system_score_gemma":0.0001814077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008003074,"about_ca_topic_score_gemma":0.001031514,"domain_scores_codex":[0.9997497,0.00004889902,0.00001060068,0.00006239268,0.0001051859,0.00002308388],"domain_scores_gemma":[0.9998326,0.00002559169,0.00003634192,0.00003762553,0.0000580765,0.000009713075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005243017,0.0001496327,0.0262249,0.0005542379,0.000302825,0.0004145839,0.0004628102,0.1111729,0.4171329,0.005798475,0.005172462,0.4320901],"study_design_scores_gemma":[0.00002800827,0.0006001638,0.03498918,0.00008705926,0.0001497726,0.0004180459,0.0001571826,0.814859,0.132223,0.002313706,0.01408816,0.00008670952],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3233111,0.001742133,0.6664016,0.0001373489,0.0003687419,0.00007044082,0.0003337169,0.002023233,0.005611666],"genre_scores_gemma":[0.8800099,0.0008746996,0.1136811,0.0000717572,0.00009015747,0.00006352445,0.0003008455,0.00002968307,0.004878424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008003074,"threshold_uncertainty_score":0.001967132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01098087138005396,"score_gpt":0.2065906968224897,"score_spread":0.1956098254424358,"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."}}