The application of support vector machine(SVM)for evaluation of the symmetry of elderly gait is investigated following the assumption that the determination of similarity between the lower limbs can be considered as a binary classification task. Adopting SVM to classify the right and left sides gait patterns of the elderly lower limbs during normal walking

the difference between the corresponding gait parameter curves can be determined effectively to assess the gait pattern symmetry. The bilateral kinetic gait data of 24 elderly participants are recorded by a force platform during normal walking. The cross-validation method is employed to evaluate the generalization ability of SVM

and three types of kernel functions(linear

polynomial and radial basis function)are validated. The experimental results demonstrate that SVM with polynomial kernel outperforms SVM with linear kernel and radial basis function in classification performance to identify the right and left sides gait patterns with an accuracy up to 88%. It is suggested that SVM serves as an efficient tool for evaluation of the symmetry of elderly gait to identify at-risk gaits in older population.