Measurement of functional impairments in human locomotion using pattern analysis
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.