{"id":"W2037855341","doi":"10.1109/embc.2012.6347551","title":"Multi-constrained inverse kinematics for the human hand","year":2012,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inverse kinematics; Kinematics; Constraint (computer-aided design); Trajectory; Computer science; Motion (physics); Motion capture; Set (abstract data type); Classification of discontinuities; Inverse dynamics; Artificial intelligence; Task (project management); Computer vision; Mathematics; Robot; 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":[0.0002776932,0.0006016768,0.000528249,0.0003567101,0.0002136228,0.0006847741,0.000410462,0.0006050781,0.001700386],"category_scores_gemma":[0.001319649,0.0003367975,0.0005545592,0.0003181888,0.0004215747,0.0004972306,0.0005594059,0.0005503689,0.0005100383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002632042,"about_ca_system_score_gemma":0.0006132678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004077075,"about_ca_topic_score_gemma":0.004255391,"domain_scores_codex":[0.9997435,0.00005374419,0.00001616299,0.00005803357,0.0001132789,0.00001529615],"domain_scores_gemma":[0.9997839,0.0001050658,0.00003556068,0.00002606105,0.00004187863,0.000007551598],"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":[0.00008109234,0.00003605324,0.0005671249,0.0002992563,0.00006581529,0.0002994722,0.000172407,0.72672,0.06085851,0.01459956,0.0007094615,0.1955912],"study_design_scores_gemma":[0.000006406395,0.00002257383,0.00040514,0.0000165612,0.000008294333,0.0001134917,0.00001813206,0.9911624,0.002694268,0.004160331,0.001378441,0.00001389067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004165587,0.0002686962,0.9945223,0.00004317624,0.00001350795,0.00001391425,0.00001915466,0.0001463947,0.0008070951],"genre_scores_gemma":[0.4729411,0.001117408,0.5200479,0.0000792588,0.00004441414,0.0001613935,0.0001254985,0.0001017168,0.005381276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004077075,"threshold_uncertainty_score":0.008106709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07807407804832459,"score_gpt":0.3074768310139965,"score_spread":0.2294027529656719,"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."}}