关键词: 外骨骼, 步态预测, 栈式自动编码器, LSTM神经网络

Abstract: A gait prediction method based on SAE and LSTM neural network is proposed to solve the problem of follow-up control of lower extremity exoskeleton robot. Since the human body has a certain periodicity of the lower limbs posture during walking, the SAE-LSTM neural network model is constructed, by using the lower extremity motion information as inputs, the gait as an output. And using the Keras to build and validate the SAE-LSTM neural network. The experimental results show that the SAE-LSTM neural network can effectively predict the gait information at the next moment according to the previous gait sequence, and the average accuracy can reach more than 92.9%.

Key words: exoskeleton, gait prediction, stacked autoencoder, Long Short-Term Memory（LSTM） neural network