{"id":"W1204921424","doi":"10.1007/s00530-015-0481-6","title":"AR-based serious game framework for post-stroke rehabilitation","year":2015,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Heart and Stroke Foundation of Canada","keywords":"Rehabilitation; Serious game; Cognition; Object (grammar); Computer science; Haptic technology; Augmented reality; Stroke (engine); Human–computer interaction; Physical medicine and rehabilitation; Video game; Multimedia; Psychology; Simulation; Physical therapy; Medicine; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006246028,0.0002032231,0.0004861566,0.0002477725,0.00004301767,0.00003892429,0.0000852743,0.000284755,0.00002476289],"category_scores_gemma":[0.006664282,0.0001680379,0.0002794016,0.0001592858,0.0001071374,0.00008841902,0.00001117615,0.000187317,0.0002063542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002695447,"about_ca_system_score_gemma":0.0003476357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001161096,"about_ca_topic_score_gemma":0.000006535859,"domain_scores_codex":[0.9982404,0.0001248292,0.0005023397,0.0003544504,0.0004581898,0.0003198445],"domain_scores_gemma":[0.9958598,0.002375797,0.0001642793,0.0004331645,0.0007651577,0.0004017715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01262599,0.003997654,0.6662909,0.01083825,0.001041647,0.00008868023,0.02899862,0.004150316,0.06100162,0.002301568,0.05429895,0.1543658],"study_design_scores_gemma":[0.02904665,0.01351622,0.1186068,0.00268685,0.0004946777,0.0001181695,0.02192077,0.5132888,0.002762564,0.0007318015,0.2954395,0.001387256],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8995903,0.001701419,0.07397959,0.005976339,0.009989837,0.006873863,0.0003890698,0.0005553036,0.0009442827],"genre_scores_gemma":[0.8337879,0.000002928106,0.1619913,0.0004056014,0.0009738304,0.0006526825,0.0001567707,0.00005655621,0.001972477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5476841,"threshold_uncertainty_score":0.797825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02511313793649644,"score_gpt":0.3047096616067912,"score_spread":0.2795965236702948,"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."}}