Clinical Human Gait Classification: Extreme Learning Machine Approach

Abstract

This study reports a novel approach for biometric gait pattern classification using Extreme Learning Machine (ELM) algorithm. Clinical gait analysis can be used for early detection of gait abnormality in brain or neurological disorder subjects. In many cases gait abnormality cannot be detected through visual observation alone, but becomes apparent only in a quantitative analysis of subject's gait. This can also help us understand the neuro-muscular mechanics associated with brain disorders. Human gait is also of profound interest to the research community in the field of biometric identification and bipedal robot locomotion due to its uniqueness and efficiency. This paper explores multi-class gait classification using four machine learning methods (KNN, SVM, ELM, MLP) and evaluates their performance for multi class gait classification. The proposed method achieves very good results. TheELM is used first time in to analyses the neuromuscular of patients suffering from multiple sclerosis and stroke.