Combining Neural Networks for Gait Classification

Abstract

Gait analysis can be defined as the numerical and graphical representation of the mechanical measurements of human walking patterns and is used for two main purposes: human identification, where it is usually applied to security issues, and clinical applications, where it is used for the non-automated and automated diagnosis of various abnormalities and diseases. Automated or semi-automated systems are important in assisting physicians for diagnosis of various diseases. In this study, a semi-automated gait classification system is designed and implemented by using joint angle and time-distance data as features. Multilayer Perceptrons (MLPs) Combination classifiers are used to categorize gait data into two categories; healthy and patient with knee osteoarthritis. Two popular approaches of combining neural networks are experimented and the results are compared according to different output combining rules. In the first one, same set is used to train all networks and afterwards the features are decomposed into five different sets. These two experiments show that using entire data set produces more accurate results than using decomposed data sets, but complexity becomes an important drawback. However, when a proper combining rule is applied to decomposed sets, results are more accurate than entire set. In this experiment sum rule produces better results than majority vote and max rules as an output combining rule.