{"id":"W2163049891","doi":"10.3390/s120708507","title":"Step Length Estimation Using Handheld Inertial Sensors","year":2012,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":227,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Accelerometer; Mobile device; Inertial measurement unit; Short-time Fourier transform; Computer science; Two step; Process (computing); Set (abstract data type); Acoustics; SIGNAL (programming language); Artificial intelligence; Simulation; Fourier transform; Mathematics; Fourier analysis","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":[0.00008890055,0.0001459929,0.0001345171,0.0001340967,0.00007893334,0.00002479427,0.00007214087,0.0001447254,0.00005296707],"category_scores_gemma":[0.00007637181,0.0001420355,0.00004390181,0.0002072041,0.00004113456,0.0001762001,0.00002025102,0.0001333182,0.0001159719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007204789,"about_ca_system_score_gemma":0.000005563558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002328516,"about_ca_topic_score_gemma":0.000003352271,"domain_scores_codex":[0.9992267,0.00001837997,0.0001846362,0.00009349725,0.0001366939,0.000340086],"domain_scores_gemma":[0.9996789,0.0000283539,0.00002657162,0.0001802476,0.00002828383,0.00005762265],"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":[0.000008681657,0.00002203978,0.002834021,0.00006673279,0.00004272298,0.00000434403,0.0008331568,0.9718083,0.008684672,0.001559086,0.0009209784,0.01321533],"study_design_scores_gemma":[0.0002091731,0.000009863052,0.0006593363,0.00001820304,0.00002214377,0.00001786291,0.0003433935,0.8926845,0.102853,0.00003547229,0.002913809,0.0002333299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732686,0.0001541996,0.02276726,0.00002483981,0.0007011556,0.000104274,0.000005204735,0.001062834,0.001911645],"genre_scores_gemma":[0.9931502,0.00001988486,0.006488705,0.00002031951,0.0001691776,0.000002648453,0.000008593252,0.00003706872,0.0001034375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0941683,"threshold_uncertainty_score":0.5792038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01640909629612529,"score_gpt":0.2352474748433178,"score_spread":0.2188383785471925,"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."}}