{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000813995,0.0007184148,0.0005654964,0.0004088548,0.0004342861,0.0006183762,0.0008567962,0.0007483973,0.001991761],"category_scores_gemma":[0.001589972,0.0002921745,0.0005833071,0.0003505751,0.0004181442,0.0008442715,0.0008159318,0.0005009658,0.0006403676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003228844,"about_ca_system_score_gemma":0.000721548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003600776,"about_ca_topic_score_gemma":0.002694008,"domain_scores_codex":[0.9989679,0.0002247561,0.00009337822,0.000251025,0.0004019537,0.0000610546],"domain_scores_gemma":[0.999436,0.0001664476,0.00007872833,0.00007255872,0.0002182317,0.00002802793],"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":[0.0008419225,0.0004621444,0.004193082,0.0008391921,0.0002730107,0.0005642878,0.001239304,0.1143732,0.2049253,0.009733843,0.005811786,0.6567429],"study_design_scores_gemma":[0.00008582138,0.0005807587,0.002396287,0.00003155088,0.00007357175,0.0003374065,0.00005973138,0.9634676,0.02364612,0.00143934,0.007812629,0.00006919241],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007094595,0.0001216995,0.9905421,0.00003678259,0.00005375108,0.00005652459,0.00001191664,0.001126113,0.0009563939],"genre_scores_gemma":[0.540277,0.0003963366,0.4532926,0.0002262449,0.00009621745,0.0004801293,0.000146586,0.00009516062,0.004989697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003600776,"threshold_uncertainty_score":0.00715965,"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."}}