{"id":"W2913330565","doi":"10.22215/etd/2014-10565","title":"A Quaternion-Based Motion Tracking and Gesture Recognition System Using Wireless Inertial Sensors","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Quaternion; Gesture recognition; Gesture; Hidden Markov model; Computer science; Computer vision; Artificial intelligence; Wearable computer; Match moving; Kalman filter; Inertial measurement unit; Tracking (education); Tracking system; Motion (physics); Augmented reality; Virtual reality; Gyroscope; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000674743,0.0005445352,0.000530833,0.000520298,0.000250573,0.000637013,0.0008259172,0.0004536503,0.003717469],"category_scores_gemma":[0.000979256,0.0003817737,0.0002956187,0.0004966626,0.0002917731,0.001221849,0.0005802004,0.0003752627,0.001874137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003177783,"about_ca_system_score_gemma":0.0005357389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002180638,"about_ca_topic_score_gemma":0.003003266,"domain_scores_codex":[0.9993204,0.000065613,0.00006711256,0.0001794195,0.0003214341,0.00004617609],"domain_scores_gemma":[0.9996219,0.00005594603,0.00006155261,0.00006380141,0.0001593751,0.00003748238],"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":[0.0002822405,0.0001501668,0.002859292,0.0002066794,0.00007639633,0.0001904309,0.000220924,0.01204451,0.3948629,0.003879314,0.003414379,0.5818128],"study_design_scores_gemma":[0.0001462951,0.001717965,0.01487153,0.0001010464,0.0002085618,0.001338223,0.0001437436,0.5230843,0.4070092,0.001918114,0.04923954,0.0002214135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02869342,0.0002878479,0.9647219,0.00008184911,0.0001235593,0.0001429356,0.0001089216,0.002745287,0.003094289],"genre_scores_gemma":[0.4395183,0.0005327634,0.5452424,0.0001945454,0.00007608915,0.0002436432,0.0004403923,0.0001439122,0.01360802],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.003717469,"threshold_uncertainty_score":0.01243621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02736935019430473,"score_gpt":0.2558366445939513,"score_spread":0.2284672943996466,"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."}}