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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. [ABSTRACT FROM AUTHOR]

Abstract: The authors have developed and tested a wearable inertial sensor system for the acquisition of gait features. The sensors were placed on anatomical segments of the lower limb: foot, shank, thigh, and hip, and the motion data were then captured in conjunction with 3D ground reaction forces (GRFs). The method of relational matrix was applied to develop a rule-based system, an intelligent fuzzy computational algorithm. The rule-based system provides a feature matrix model representing the strength of association or interaction amongst the elements of the gait functions (limb-segments accelerations and GRFs) throughout the gait cycle. A comparison between the reference rule-based data and an input test data was evaluated using a fuzzy similarity algorithm. This system was tested and evaluated using two subject groups: 10 healthy subjects were recruited to establish the reference fuzzy rule-base, and 4 relapsing remitting multiple sclerosis subjects were used as an input test data; and the grade of similarity between them was evaluated. This similarity provides a quantitative assessment of mobility state of the impaired subject. This algorithmic tool may be helpful to the clinician in the identification of pathological gait impairments, prescribe treatment, and assess the improvements in response to therapeutic intervention. [Copyright &y& Elsevier]

Abstract: The authors have developed a measurement methodology for the efficient and reliable analysis of human gait dynamics at a level that quantifies variations from morphometrically adjusted normal in three dimensions and in real-time. The large quantities of acquired kinetic, kinematic and electromyographic data are dealt with as aggregate information granules, enabling an efficient partition of input space and more rapid analysis. The process of data aggregation or granulation is predicted on an understanding and application of the functional relationships of human movement and are expressed in an implication table. This implication table is the basis of a fuzzy relational matrix, a feature pattern, established between the dynamic activities during locomotion. The performance and efficiency of the system have been evaluated using two case studies of gait impairments involving two patients each with cerebral palsy and with multiple sclerosis, respectively. [ABSTRACT FROM AUTHOR]