Automatic Neuro Disease Classification Based on Gait Analysis using Bi-stacked GRU

Abstract

Deep learning is a branch of machine learning based on deep neural network used to train the computers without being explicitly programmed. Recurrent neural network (RNN) is a part of deep learning methods which is the first algorithm in deep learning that produce output based on the sequence of input. RNN have multiple advantages in the field of medicine to solve it. Long short term memory (LSTM) an extension of RNN solves vanishing gradient and exploding problem in RNN by using to store the long sequence of memory through cell state. Neurodegenerative disease affects the neurons in Human brain which are the blocks of nervous system includes brain and spinal cord, if it is die or damaged can’t be replaced and motion of two lower limbs causing gait disorder. Such diseases are treated with LSTM model, but accurate results are not able to achieve due to gradient exploding problem. To improve the accuracy we proposed the variants of Gated