{"id":"W4402713024","doi":"10.1016/j.medengphy.2024.104238","title":"Development of a closed-loop controller for functional electrical stimulation therapy plus visual feedback balance training for standing balance training","year":2024,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Balance (ability); Functional electrical stimulation; Visual feedback; Training (meteorology); Physical medicine and rehabilitation; Closed loop; Balance training; Dynamic balance; Stimulation; Control theory (sociology); Physical therapy; Medicine; Psychology; Computer science; Neuroscience; Control (management); Control engineering; Artificial intelligence; Engineering; Physics","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":[0.0003240074,0.0003955557,0.0003122203,0.0002465443,0.0002457235,0.0003656174,0.0009017088,0.0004650629,0.002732105],"category_scores_gemma":[0.0006422076,0.0001955255,0.0002185648,0.00009608408,0.0001731977,0.0002214567,0.0003566269,0.0002489803,0.0006256424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002015557,"about_ca_system_score_gemma":0.0005041144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001802164,"about_ca_topic_score_gemma":0.002060006,"domain_scores_codex":[0.9997233,0.00002890543,0.0000226678,0.00007113587,0.000130044,0.00002396198],"domain_scores_gemma":[0.9997517,0.00005824157,0.00003534811,0.00001428852,0.0001217588,0.00001853376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006838181,0.0007588119,0.003159624,0.0006844209,0.0001531886,0.000502411,0.0003006314,0.03818739,0.5455564,0.001579826,0.002871797,0.4055617],"study_design_scores_gemma":[0.0007195926,0.006807191,0.0185231,0.0001990379,0.00029114,0.001250676,0.0001426927,0.7118422,0.2295585,0.0008613581,0.02966245,0.0001419856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08562449,0.0004145202,0.9043088,0.0001390209,0.0002013211,0.0007068463,0.0001029936,0.002875229,0.005626872],"genre_scores_gemma":[0.8469145,0.0002045898,0.1451287,0.0001729492,0.00004542172,0.0009016444,0.0001891883,0.00006983856,0.006373059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002732105,"threshold_uncertainty_score":0.009139776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06543559668494336,"score_gpt":0.3665939189978761,"score_spread":0.3011583223129328,"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."}}