{"id":"W2986336325","doi":"10.22215/etd/2017-11949","title":"Human Body Structure Calibration Using Wearable Inertial Sensors","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Calibration; Computer science; Wearable computer; Accelerometer; Torso; Inertial measurement unit; Inertial frame of reference; Computer vision; Simulation; Process (computing); Artificial intelligence; Embedded system","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":[0.0005307111,0.0008322098,0.0004107675,0.0005944122,0.0001951759,0.0005283756,0.0007247837,0.0005213683,0.00149731],"category_scores_gemma":[0.00201206,0.0003197619,0.0003491108,0.0005176204,0.0002789507,0.0008451697,0.000766856,0.0003437058,0.0008550252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001448172,"about_ca_system_score_gemma":0.0002276136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004596793,"about_ca_topic_score_gemma":0.0005748873,"domain_scores_codex":[0.9990667,0.0001827937,0.00004966875,0.0002004344,0.0004593094,0.00004117064],"domain_scores_gemma":[0.9995547,0.00009628889,0.00009320107,0.0001032944,0.0001381386,0.00001442876],"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":[0.0002696294,0.0001298576,0.008567279,0.000503671,0.00009637031,0.000224372,0.0005981483,0.01292468,0.4908544,0.002166239,0.001527296,0.4821381],"study_design_scores_gemma":[0.00009049611,0.00178543,0.0752684,0.0002431419,0.000226086,0.00264179,0.0006213028,0.1987937,0.6830433,0.002975935,0.03410086,0.0002096118],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1011556,0.0007074656,0.8918808,0.00008897627,0.0001307926,0.0001680359,0.0001243406,0.001235494,0.004508584],"genre_scores_gemma":[0.7983337,0.00116357,0.1954657,0.0001218692,0.00007298201,0.0002052968,0.0002661168,0.0001430447,0.004227813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00149731,"threshold_uncertainty_score":0.005008936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540322536467976,"score_gpt":0.3050237760499797,"score_spread":0.2796205506852999,"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."}}