{"id":"W2908523746","doi":"10.1109/lra.2019.2894005","title":"HMFP-DBRNN: Real-Time Hand Motion Filtering and Prediction via Deep Bidirectional RNN","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Concordia University","funders":"","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Noise (video); Ground truth; Compensation (psychology); Motion (physics); Machine learning; Artificial neural network","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":[0.0004330765,0.0008984443,0.0005064221,0.0002661158,0.0001921694,0.0003860962,0.001174075,0.0008601762,0.002021146],"category_scores_gemma":[0.001061332,0.0003488109,0.0004288641,0.0002700635,0.0002522187,0.0006253994,0.000614868,0.00106963,0.001176129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004602719,"about_ca_system_score_gemma":0.0006781278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01208434,"about_ca_topic_score_gemma":0.01835847,"domain_scores_codex":[0.9998432,0.00002303183,0.000008823033,0.0000512914,0.0000445223,0.00002906087],"domain_scores_gemma":[0.9998003,0.00007382617,0.0000249268,0.00002877612,0.00005507384,0.00001719074],"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":[0.0002413532,0.0001292681,0.0009634454,0.0001359022,0.00008643301,0.0001571281,0.00006774048,0.3112515,0.03444243,0.003881717,0.006514917,0.6421283],"study_design_scores_gemma":[0.000005235679,0.00002690914,0.0001256718,0.00000684755,0.000007471002,0.00002456844,0.000003007323,0.9956352,0.002805538,0.0007023761,0.0006515809,0.000005456917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01805086,0.0009166191,0.9747192,0.0001800091,0.0001344011,0.00004467286,0.0001742023,0.003032751,0.002747194],"genre_scores_gemma":[0.5989403,0.0007802089,0.3869204,0.0004275255,0.0001235688,0.0001453378,0.0009461358,0.0002765968,0.01143998],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01208434,"threshold_uncertainty_score":0.024028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008782471070156487,"score_gpt":0.218577169823623,"score_spread":0.2097946987534665,"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."}}