{"id":"W4289656161","doi":"10.1109/tim.2022.3196121","title":"A Natural Bare-Hand Interaction Method With Augmented Reality for Constraint-Based Virtual Assembly","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Modern Agriculture Industry Technology System; National Natural Science Foundation of China","keywords":"Computer science; Gesture; Augmented reality; Virtual reality; Operator (biology); Process (computing); Constraint (computer-aided design); Human–computer interaction; Interaction technique; Interface (matter); Naturalness; Kalman filter; Computer vision; 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":[0.0003965828,0.0009976284,0.0005702208,0.0006398207,0.0003969841,0.0008700051,0.001089427,0.0006813403,0.00490279],"category_scores_gemma":[0.001218072,0.0004401785,0.0009813817,0.0004472875,0.0004104837,0.0008816624,0.001451123,0.0005743237,0.0008749653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002051625,"about_ca_system_score_gemma":0.0004732578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001905953,"about_ca_topic_score_gemma":0.002459884,"domain_scores_codex":[0.9988732,0.0002457946,0.00006098888,0.0002272608,0.0005155307,0.00007735811],"domain_scores_gemma":[0.9995059,0.000153552,0.00005939425,0.0001188907,0.0001251439,0.00003722626],"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":[0.0006185759,0.0003545853,0.001299422,0.0005916353,0.0001573981,0.0006682095,0.001198057,0.04016122,0.3120735,0.006182294,0.004488759,0.6322063],"study_design_scores_gemma":[0.0001105322,0.0009201071,0.00616277,0.00005704551,0.0001434942,0.002078851,0.0003215349,0.8508923,0.1100452,0.001869974,0.02710414,0.0002939908],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01416269,0.0001522515,0.9825816,0.00003379608,0.00004605565,0.00006310725,0.00004364456,0.00124259,0.00167427],"genre_scores_gemma":[0.3049025,0.0002826609,0.6898814,0.00008955526,0.0000448503,0.0002280592,0.0002423752,0.0002128358,0.004115857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00490279,"threshold_uncertainty_score":0.01640141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05865719915629326,"score_gpt":0.316943917968295,"score_spread":0.2582867188120018,"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."}}