User intent recognition for transfemoral amputees with prosthetic legs using evolutionary algorithms

User intent recognition (UIR) enables transfemoral amputees to walk reliably and seamlessly with prosthetic legs. The objective of this paper is to design a UIR system that is optimal in terms of both accuracy and parsimony. We propose the application of two methods to achieve this goal. The first is a filter method, Fisher’s linear discriminant score (FLDS); and the second is a wrapper method, linear discriminant analysis (LDA). Both methods are combined with the evolutionary algorithm biogeography-based optimization (BBO) to find optimal feature subsets. The optimal subsets are then compared with a current state-of-the-art feature selection method, in conjunction with several powerful linear and nonlinear classifiers that are used to identify level ground walking at various speeds. Classification performance is enhanced with a majority voting filter. The best performance is achieved with a multi-class support vector machine that is trained with FLDS/BBO feature subset and that reduces the number of required features by up to about 73% and attains a mean prediction accuracy of 98.94% for amputee subjects. Results show the capability of advanced subset selection methods to construct a UIR system with simultaneous minimum complexity and maximum performance.