Affiliations: Department of Electrical and Computer Engineering, The University of Texas at El Paso, TX, USA | Computational Science Program, The University of Texas at El Paso, TX, USA | Mentis Rehabilitation Hospital, El Paso, TX, USA | Paul L. Foster School of Medicine, Texas Tech University, El Paso, TX, USA
Note: [] Corresponding author. Thompson Sarkodie-Gyan, Department of Electrical and Computer Engineering, The University of Texas at El Paso, Engineering Annex, Room A-316, 500 W. University Avenue, El Paso, TX 79968, USA. Tel.: +1 915 747 7011; Fax: +1 915 747 7871; E-mail: [email protected]
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.
