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.