{"id":"W2968774000","doi":"10.3390/s19163474","title":"Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rehabilitation; Hemiparesis; Physical medicine and rehabilitation; Protocol (science); Task (project management); Training (meteorology); Computer science; Set (abstract data type); Work (physics); Robotic arm; Simulation; Stroke (engine); Physical therapy; Artificial intelligence; Medicine; Engineering; Surgery","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":[0.0002025295,0.0004224666,0.0003746447,0.0003658013,0.0001019827,0.0001941537,0.0003520496,0.0003914135,0.003389307],"category_scores_gemma":[0.0003453731,0.000181374,0.0002880822,0.0002512045,0.0001135835,0.0003902877,0.0003731211,0.0001587592,0.0004328495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001147056,"about_ca_system_score_gemma":0.0002878667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001445808,"about_ca_topic_score_gemma":0.003256717,"domain_scores_codex":[0.9998235,0.00002030122,0.00001489007,0.00004634302,0.00007750205,0.00001753974],"domain_scores_gemma":[0.9999359,0.00001675893,0.00001325982,0.000005773388,0.00001846681,0.000009831904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002459898,0.0006903718,0.006948664,0.00153922,0.0000904566,0.0003817944,0.000295061,0.007979959,0.4942618,0.0004332213,0.002715162,0.4822044],"study_design_scores_gemma":[0.0006612453,0.006147992,0.2698681,0.0005078223,0.0004503345,0.003108324,0.0006111216,0.2840696,0.4144917,0.001880003,0.01776446,0.0004392758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6617065,0.002681773,0.323303,0.0002803649,0.0001809892,0.0005739793,0.002548257,0.002473399,0.006251833],"genre_scores_gemma":[0.9129388,0.000966688,0.07986843,0.0001500486,0.00001969007,0.0005657955,0.0004915712,0.00007233698,0.004926598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003389307,"threshold_uncertainty_score":0.01133835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03055360400079636,"score_gpt":0.2911751589743189,"score_spread":0.2606215549735225,"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."}}