{"id":"W4386811517","doi":"10.1016/j.mlwa.2023.100499","title":"A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms","year":2023,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Machine learning; Computer science; Artificial intelligence; Naive Bayes classifier; Support vector machine; Random forest; Rehabilitation; Perceptron; Biofeedback; Artificial neural network; Algorithm; Physical medicine and rehabilitation; Medicine; Physical therapy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005843031,0.0006270129,0.0006018494,0.0005590402,0.0003736341,0.0007995813,0.001201454,0.001161598,0.00357269],"category_scores_gemma":[0.001131043,0.0002871918,0.0003872239,0.0003500301,0.0003399803,0.001061557,0.0004297467,0.000667933,0.001538659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004185339,"about_ca_system_score_gemma":0.0006528134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002644323,"about_ca_topic_score_gemma":0.002625235,"domain_scores_codex":[0.9993837,0.00009700518,0.00004276028,0.0001915188,0.0002396923,0.00004532265],"domain_scores_gemma":[0.999621,0.0001129671,0.0000367394,0.00006934322,0.0001391745,0.0000207675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003475054,0.0006561672,0.002697255,0.0002148399,0.0001023128,0.0002800327,0.0001847526,0.0216526,0.110318,0.00234353,0.003604885,0.8575981],"study_design_scores_gemma":[0.00008257577,0.0008163413,0.004766104,0.00009430799,0.00008643119,0.0007194063,0.00008952511,0.8981006,0.07984492,0.002505103,0.01282253,0.00007213151],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01910282,0.000196575,0.9738818,0.0002023163,0.0001155089,0.000174525,0.00005775398,0.004164519,0.002104147],"genre_scores_gemma":[0.3729079,0.000301577,0.6201338,0.0004444633,0.00007309559,0.0003457161,0.0001355393,0.00009362881,0.005564348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00357269,"threshold_uncertainty_score":0.01195186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02244395339043768,"score_gpt":0.2981328769550102,"score_spread":0.2756889235645725,"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."}}