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báo cáo khoa học: " A Dynamic Neuro-Fuzzy Model Providing Bio-State Estimation and Prognosis Prediction for Wearable Intelligent Assistants"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: A Dynamic Neuro-Fuzzy Model Providing Bio-State Estimation and Prognosis Prediction for Wearable Intelligent Assistants | Journal of NeuroEngineering and Rehabilitation BioMed Central Research Open Access A Dynamic Neuro-Fuzzy Model Providing Bio-State Estimation and Prognosis Prediction for Wearable Intelligent Assistants Yu Wang t and Jack M Winterst Address Department of Biomedical Engineering Marquette University Milwaukee WI USA Email Yu Wang - yu.wang@mu.edu Jack M Winters - jack.winters@mu.edu Corresponding author fEqual contributors Published 28 June 2005 Received 10 February 2005 Journal of NeuroEngineering and Rehabilitation 2005 2 15 doi l0.ll86 l743- Accepted 28 June 2005 0003-2-15 This article is available from http www.jneuroengrehab.cOm content 2 1 15 2005 Wang and Winters licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Intelligent management of wearable applications in rehabilitation requires an understanding of the current context which is constantly changing over the rehabilitation process because of changes in the person s status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents assistants WIAs . Methods The model structure includes the following types of signals inputs states outputs and outcomes. Inputs are facts or events which have effects on patients physiological and rehabilitative states different classes of inputs e.g. facts context medication therapy have different nonlinear mappings to a fuzzy effect. States are dimensionless linguistic fuzzy variables that change based on causal rules as implemented by a fuzzy inference system FIS . The FIS with rules based on expertise and evidence essentially defines the nonlinear state equations that are .