{"id":"W2088709514","doi":"10.1109/bwcca.2012.75","title":"Motion Tracking and Learning in Telerehabilitation Applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Telerehabilitation; Computer science; Tracking (education); Match moving; Motion (physics); Motion sensors; Work (physics); Artificial intelligence; Track (disk drive); Computer vision; Telemedicine; Health care; Engineering","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":[0.0006430326,0.0002927252,0.0003375915,0.0008973309,0.0003183305,0.0007726277,0.0005040276,0.001233392,0.002750102],"category_scores_gemma":[0.00214786,0.000212047,0.0002670209,0.001164726,0.0004409355,0.001250975,0.0003920291,0.0004846707,0.0007251198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004701048,"about_ca_system_score_gemma":0.0003409243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004179243,"about_ca_topic_score_gemma":0.003272323,"domain_scores_codex":[0.9995856,0.0001182737,0.00003162962,0.0001154922,0.000113807,0.00003527954],"domain_scores_gemma":[0.9992869,0.0004134206,0.00006835287,0.00006058114,0.0001435465,0.00002720835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002909472,0.0001240069,0.004775294,0.000280011,0.00003858596,0.0002593361,0.0002172272,0.07140347,0.01685728,0.01370477,0.00305674,0.8889923],"study_design_scores_gemma":[0.00003075176,0.0003647875,0.01509846,0.0001665174,0.00005727788,0.0006690383,0.0002625076,0.9045932,0.02161527,0.02786647,0.02920282,0.00007273866],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04763726,0.01307405,0.9253225,0.001077664,0.000197276,0.0001007754,0.0001243205,0.0009073493,0.01155889],"genre_scores_gemma":[0.7532645,0.01200695,0.2198647,0.000351849,0.0004221018,0.0001171536,0.0002682667,0.00008688353,0.01361756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004179243,"threshold_uncertainty_score":0.009199977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646511299071206,"score_gpt":0.2943965925827471,"score_spread":0.2779314795920351,"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."}}