A dynamic neuro-fuzzy model providing bio-state estimation and prognosis prediction for wearable intelligent assistants.
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 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.


RESULTS
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.


CONCLUSIONS
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.
Wang
Yu
Y
Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA. yu.wang@mu.edu
Winters
Jack M
JM
D003661:Decision Support Techniques; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D055070:Resistance Training / Q000295:instrumentation / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000503:physiopathology; D000071939:Stroke Rehabilitation