{"id":"W2989911746","doi":"10.1109/thms.2019.2947576","title":"Natural Human–Robot Interface Using Adaptive Tracking System with the Unscented Kalman Filter","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Kalman filter; Computer science; Robot; Interface (matter); Cartesian coordinate system; Process (computing); Task (project management); Filter (signal processing); Noise (video); Computer vision; Tracking (education); Simulation; Artificial intelligence; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006714552,0.0005569276,0.0006517287,0.0003763918,0.001014147,0.0005988016,0.001128031,0.0001595247,0.00002540802],"category_scores_gemma":[0.000001602803,0.0003631267,0.000245999,0.0006910052,0.00009291386,0.0007950464,0.00001083787,0.0009050381,0.0002297344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003959039,"about_ca_system_score_gemma":0.00006327665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001026848,"about_ca_topic_score_gemma":0.0005131855,"domain_scores_codex":[0.9962809,0.0006930878,0.0007166509,0.0008884676,0.0008347229,0.0005861891],"domain_scores_gemma":[0.9976595,0.00023472,0.0004414409,0.001199013,0.0003174447,0.0001478762],"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":[0.0003794818,0.001019819,0.0003792464,0.002066175,0.002407627,0.0001972543,0.01556946,0.8124506,0.1460971,0.01367706,0.0005006609,0.005255498],"study_design_scores_gemma":[0.005937205,0.001879853,0.000555144,0.006840782,0.0003230181,0.002098394,0.008192701,0.9452827,0.0245718,0.00001517693,0.001760916,0.002542301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06979683,0.000335804,0.9230546,0.0001025943,0.003183027,0.001657589,0.00003874023,0.0005854813,0.001245384],"genre_scores_gemma":[0.9977573,0.000001120219,0.0003304268,0.00005755647,0.0001923688,0.0001071589,0.000005783762,0.00007008573,0.001478222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9279605,"threshold_uncertainty_score":0.999882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04389706553892932,"score_gpt":0.2913349211407628,"score_spread":0.2474378556018335,"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."}}