{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009816229,0.0005034104,0.0003662481,0.0003987983,0.0001176973,0.0002895504,0.0004923578,0.0004081719,0.0009671923],"category_scores_gemma":[0.0004233824,0.0001784834,0.0003851395,0.0003164903,0.000104268,0.0003516613,0.0002272303,0.0002287103,0.0005303508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001424471,"about_ca_system_score_gemma":0.0002031653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002521785,"about_ca_topic_score_gemma":0.003069097,"domain_scores_codex":[0.999892,0.00001740822,0.000005723992,0.00003484184,0.00004151016,0.000008488111],"domain_scores_gemma":[0.9999121,0.00003017059,0.00001580175,0.00001520852,0.00002189755,0.000004651674],"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":[0.0005640246,0.0001647672,0.01706278,0.000449308,0.0002664969,0.0005124151,0.0001789757,0.3212309,0.1730078,0.001202062,0.001716822,0.4836436],"study_design_scores_gemma":[0.00003052875,0.0003576805,0.02548954,0.00003805547,0.00008731936,0.0004857999,0.00006273742,0.9361303,0.03362954,0.000746426,0.002899508,0.00004256669],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1757147,0.0005473227,0.8205048,0.0000576925,0.00006100351,0.00007546756,0.0003143056,0.001111624,0.001613094],"genre_scores_gemma":[0.8887819,0.0005337274,0.1070703,0.00004001463,0.00002436277,0.00009903009,0.0004756525,0.0000405974,0.002934393],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002521785,"threshold_uncertainty_score":0.005014241,"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."}}