计算机工程与应用2019，Vol.55Issue(12)：110-116,1,8.DOI:10.3778/j.issn.1002-8331.1811-0315
基于SAE和LSTM的下肢外骨骼步态预测方法
Gait Prediction Method of Lower Extremity Exoskeleton Based on SAE and LSTM Neural Network
摘要
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 pos-ture during walking, the SAE-LSTM neural network model is constructed, by using the lower extremity motion informa-tion as inputs, the gait as an output. And using the Keras to build and validate the SAE-LSTM neural network. The experi-mental 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%.关键词
外骨骼/步态预测/栈式自动编码器/LSTM神经网络Key words
exoskeleton/ gait prediction/ stacked autoencoder/ Long Short-Term Memory(LSTM)neural network分类
信息技术与安全科学引用本文复制引用
陈超强,蒋磊,王恒..基于SAE和LSTM的下肢外骨骼步态预测方法[J].计算机工程与应用,2019,55(12):110-116,1,8.基金项目
北京市自然科学基金(No.3182031). （No.3182031）
