Wang Yu, Winters Jack M
Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA.
J Neuroeng Rehabil. 2005 Jun 28;2:15. doi: 10.1186/1743-0003-2-15.
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).
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 implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states.
The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training.
A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation if sufficient evidence and/or expert knowledge are available. It is intended to provide context-awareness of changing status through state estimation, which is critical information for WIA's to be effective.
康复中可穿戴应用的智能管理需要了解当前情境，由于患者状态和环境的变化，该情境在康复过程中不断改变。本文提出了一种实现基于专家和证据推理的动态递归神经模糊系统。其旨在为可穿戴智能代理/助手（WIA）提供情境感知。
模型结构包括以下几种信号类型：输入、状态、输出和结果。输入是对患者生理和康复状态有影响的事实或事件；不同类别的输入（如事实、情境、药物、治疗）对模糊“效果”有不同的非线性映射。状态是无量纲的语言模糊变量，根据因果规则变化，由模糊推理系统（FIS）实现。基于专业知识和证据的规则的FIS本质上定义了由动态神经元核心实现的非线性状态方程。输出是使用传统或模糊映射对状态和有效输入进行加权的函数，可执行动作、预测性能或协助决策。结果是要最大化的标量，是输出和状态的函数。
第一个例子展示了在大规模中风神经康复应用中的设置和使用（有16个输入、12个状态、5个输出和3个结果），展示了这种建模工具如何能成功捕捉与情境相关状态（如损伤、疼痛）中因果动态变化作为输入事件模式（如药物）的函数。第二个例子展示了使用科学证据开发基于规则的动态模型，这里用于预测短期疲劳和长期力量训练时肌肉力量的变化。
开发了一种神经模糊建模框架用于估计康复变化，如果有足够的证据和/或专家知识，可应用于任何康复领域。其旨在通过状态估计提供对变化状态的情境感知，这是WIA有效运作的关键信息。
