{"id":"W3034428823","doi":"10.2196/17289","title":"Rehabilitation Exergames: Use of Motion Sensing and Machine Learning to Quantify Exercise Performance in Healthy Volunteers","year":2020,"lang":"en","type":"article","venue":"JMIR Rehabilitation and Assistive Technologies","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Imperial College London","keywords":"Dynamic time warping; Hidden Markov model; Rehabilitation; Motion capture; Physical medicine and rehabilitation; Computer science; Motion (physics); Functional movement; Physical therapy; Artificial intelligence; Machine learning; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006944156,0.0005846725,0.0003363677,0.0008215714,0.0001398629,0.0002696678,0.0002690266,0.0005432529,0.001155675],"category_scores_gemma":[0.001191172,0.0001904271,0.0002359659,0.000255049,0.0002470585,0.0002847434,0.0005025946,0.0001807954,0.0003021798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001326427,"about_ca_system_score_gemma":0.000160703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007265048,"about_ca_topic_score_gemma":0.00135852,"domain_scores_codex":[0.9997022,0.00009952206,0.00002244248,0.00009170212,0.00006195155,0.00002225104],"domain_scores_gemma":[0.9997286,0.0001051697,0.000053715,0.00001508961,0.00005495746,0.00004237438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.009080824,0.003503567,0.1675361,0.001586065,0.0005921015,0.000466351,0.001210679,0.01262337,0.4032654,0.0005617887,0.001055985,0.3985176],"study_design_scores_gemma":[0.0003193326,0.0181226,0.8719202,0.00008992572,0.0002513483,0.0018091,0.0004638043,0.07138769,0.03373769,0.0005606358,0.001237342,0.0001003932],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729825,0.0002875835,0.0248415,0.00004283247,0.00002545686,0.0004227654,0.0004065468,0.0001547803,0.0008361755],"genre_scores_gemma":[0.9696213,0.0001967409,0.02820723,0.00005615805,0.00003137242,0.0005179873,0.000348905,0.00001635869,0.001003985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001155675,"threshold_uncertainty_score":0.003866136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02686718766707355,"score_gpt":0.2627801883127804,"score_spread":0.2359130006457069,"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."}}