{"id":"W3184086089","doi":"10.1007/978-3-030-77414-1_27","title":"Exploring Coordination Patterns in VR-Based Rehabilitation for Stroke Using the Kinect Sensor","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interactivity; Computer science; Rehabilitation; Human–computer interaction; Virtual reality; Motion (physics); Motion capture; Motion analysis; Multimedia; Artificial intelligence; Medicine","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":[0.0001588082,0.000433074,0.0003441689,0.0004679806,0.0001318411,0.0006184304,0.0003511993,0.0003624451,0.004989878],"category_scores_gemma":[0.0004465124,0.0002793688,0.0003268468,0.0007792079,0.0001631642,0.0004540241,0.0004334912,0.0002179885,0.0009057575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263646,"about_ca_system_score_gemma":0.0002947334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002046273,"about_ca_topic_score_gemma":0.004384574,"domain_scores_codex":[0.9998704,0.00001999712,0.000008381293,0.00004213633,0.00004700455,0.00001215542],"domain_scores_gemma":[0.9999237,0.00004055505,0.00001053419,0.000006235162,0.00001075578,0.000008324003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008704718,0.0001545919,0.006274038,0.001057232,0.00009382983,0.0003582709,0.001023571,0.03743939,0.2666643,0.002128011,0.005190773,0.6787454],"study_design_scores_gemma":[0.0001939463,0.001776252,0.2656048,0.0008942437,0.000294278,0.004486899,0.002388438,0.5066794,0.1552762,0.01416793,0.04791968,0.0003179073],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3831951,0.005900968,0.5786972,0.0003304499,0.0001732331,0.0002209328,0.00346737,0.002213718,0.02580094],"genre_scores_gemma":[0.7686036,0.004538148,0.2086314,0.00009519422,0.00003815,0.0001695307,0.001308118,0.0003084244,0.01630749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004989878,"threshold_uncertainty_score":0.01669288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06848214305795147,"score_gpt":0.3021876791229307,"score_spread":0.2337055360649792,"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."}}