{"id":"W2545812078","doi":"10.1109/gem.2014.7048108","title":"Quaternion based gesture recognition using worn inertial sensors in a motion tracking system","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Hidden Markov model; Computer science; Gesture recognition; Gesture; Quaternion; Wearable computer; Markov chain; Computer vision; Artificial intelligence; Context (archaeology); Inertial measurement unit; Orientation (vector space); Activity recognition; Speech recognition; Machine learning; Embedded system; Mathematics","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":[0.001046946,0.0001805334,0.0002423511,0.0003538898,0.00008586131,0.0002177572,0.0002286637,0.0001672989,0.000009809656],"category_scores_gemma":[0.00007482525,0.0001591543,0.00007090389,0.0005422154,0.0000127004,0.0005579517,0.00003698064,0.0001736267,0.0001251599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711266,"about_ca_system_score_gemma":0.00003135752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002753612,"about_ca_topic_score_gemma":0.0001417809,"domain_scores_codex":[0.9979194,0.0005597027,0.0004417965,0.0004535697,0.0003352897,0.0002901733],"domain_scores_gemma":[0.9991744,0.0001239044,0.0001599117,0.0003027278,0.0001511437,0.00008792055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000879092,0.0003033974,0.01350988,0.0007232702,0.00003148741,0.00008176274,0.002218835,0.0179886,0.02436793,0.001525815,0.0001063156,0.9390548],"study_design_scores_gemma":[0.0009209508,0.0000448273,0.005951005,0.000581521,0.000007599988,0.00007736623,0.0001270299,0.9819379,0.009796833,0.00006775901,0.0001954892,0.0002916715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3608896,0.000008121249,0.6370403,0.0001506335,0.0005095554,0.0002081817,0.000001075113,0.000263261,0.000929319],"genre_scores_gemma":[0.9838442,4.902301e-7,0.01570475,0.000122311,0.0002663186,0.00001675685,0.00001160283,0.00001482502,0.00001871431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9639493,"threshold_uncertainty_score":0.6490124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0393123997400965,"score_gpt":0.2456100265137494,"score_spread":0.2062976267736529,"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."}}