{"id":"W2313123462","doi":"10.1109/embc.2014.6943943","title":"Improving rehabilitation exercise performance through visual guidance","year":2014,"lang":"en","type":"article","venue":"","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Usability; Computer science; Rehabilitation; Motion (physics); Visual feedback; Visualization; Motion capture; Inertial measurement unit; Human–computer interaction; Physical medicine and rehabilitation; Artificial intelligence; Computer vision; Physical therapy; 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":[0.0003948,0.0005766823,0.0002429184,0.0004504879,0.0001297009,0.0005236709,0.0005294893,0.0006585995,0.002853903],"category_scores_gemma":[0.002225959,0.0001328591,0.0002571126,0.0001732809,0.0002011085,0.0004226187,0.0005506413,0.0001934325,0.0007018105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001091823,"about_ca_system_score_gemma":0.0002656069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001105353,"about_ca_topic_score_gemma":0.001605443,"domain_scores_codex":[0.9995828,0.0001154044,0.00002584577,0.00007859278,0.0001558346,0.00004142738],"domain_scores_gemma":[0.999245,0.0004386233,0.00009534133,0.00004251025,0.0001367361,0.00004176507],"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":[0.00142568,0.0009023984,0.005774096,0.001073337,0.00004928781,0.000302251,0.0007258689,0.003554042,0.28287,0.000399629,0.00277741,0.7001459],"study_design_scores_gemma":[0.001816377,0.02994189,0.2434585,0.002138583,0.0006338168,0.007957252,0.001697203,0.09249562,0.5185187,0.003481086,0.09715857,0.0007023553],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6703818,0.004620281,0.3024091,0.000730258,0.0002008643,0.0005094052,0.0004209699,0.006600589,0.01412676],"genre_scores_gemma":[0.9030404,0.001554501,0.08972462,0.0002686535,0.00008412285,0.000252501,0.0001887227,0.0001206531,0.004765878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002853903,"threshold_uncertainty_score":0.009547234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01592472538389162,"score_gpt":0.3521748586206776,"score_spread":0.336250133236786,"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."}}