Application of fuzzy sets for assisting the physician's model of functional impairments in human locomotion

Abstract

The authors have developed a system to assist clinicians reliably assess, at an early post-insult stage, the degree of disability the patient will ultimately experience. Physician decision processes offered to date, especially those relative to diagnosis and patient treatment, suffer from the inability to incorporate all useful data on the patient. We present a computational intelligence algorithm based on fuzzy clustering (the theory of fuzzy sets and systems) techniques to aid the physician to evaluate the complete representation of information emanating from the measured kinetic, kinematics and electromyographic data from the patient. The fuzzy clustering technique helps develop membership functions as an optimization task. The calculated membership grades are organized in the form of optimized partition matrix. As the optimization method operates on available data, it attempts to reflect their characteristics in the resulting constructs, e.g. a distribution of the prototypical values of the clusters.

Collective intelligence and databases in eHealth: A survey1

“…One of them is the search of improvements and solutions for illnesses. An important contribution consists in developing systems to assess the degree of disability of pa-tients [49]. Previous work only took into account few data of the patient and general information, but specific data related to locomotion was oversaw.…”

Operations on complex multi-fuzzy sets

“…Fuzzy set theory generalised the range values of classical set theory from the integer 0 and 1 to the interval [0,1]. In addition, this concept has proven to be very useful in many different fields [2][3][4].…”