{"id":"W2219948256","doi":"10.1109/tim.2015.2498560","title":"Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":265,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gesture; Dynamic time warping; Gesture recognition; Computer science; Computer vision; Artificial intelligence; Sign (mathematics); Interface (matter); Sign language; Hidden Markov model; Pixel; Object (grammar); Speech recognition; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008248935,0.0001691074,0.0001774069,0.0002905397,0.000161694,0.0002288194,0.0001644829,0.0000687903,0.000005629342],"category_scores_gemma":[0.00001224415,0.0001679699,0.00001767619,0.0002844229,0.00004564534,0.0008674471,0.000006758265,0.0001554868,0.00002132926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002961019,"about_ca_system_score_gemma":0.0001342373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009323702,"about_ca_topic_score_gemma":0.0009181669,"domain_scores_codex":[0.9983174,0.0001831837,0.0003343535,0.0004590121,0.0005117392,0.0001943539],"domain_scores_gemma":[0.9992771,0.00003123978,0.0001048602,0.0002942163,0.0001374449,0.0001551395],"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":[0.00007619999,0.0002754395,0.0001332879,0.0001265424,0.00009435355,0.00001174785,0.004487811,0.001829754,0.01736365,0.00000915499,0.00004443662,0.9755476],"study_design_scores_gemma":[0.005796063,0.0003444795,0.002337675,0.0007981353,0.0001068484,0.0002183748,0.001744465,0.9803607,0.006589312,0.0007426664,0.0003237681,0.0006374541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2401674,0.00009442984,0.7583214,0.0004245866,0.0004046534,0.0004295739,0.00003080741,0.00005382154,0.00007333608],"genre_scores_gemma":[0.9881017,0.00007866571,0.01154259,0.0001798391,0.000008529269,0.00002973152,0.00002523991,0.00001192695,0.00002183057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.978531,"threshold_uncertainty_score":0.6849613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1452773066267039,"score_gpt":0.3093305887767824,"score_spread":0.1640532821500785,"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."}}