基于SAE和LSTM的下肢外骨骼步态预测方法

提出一种基于栈式自动编码器（Stacked Auto Encoder，SAE）和长短时记忆（Long Short-Term Memory，LSTM）神经网络相结合的步态预测方法解决下肢外骨骼机器人跟随控制问题。人体在行走过程中下肢步态具有一定的周期性，通过将下肢运动信息作为输入，步态作为输出，构建SAE-LSTM神经网络模型，并利用Keras对SAE-LSTM神经网络进行搭建和验证。实验结果表明，SAE-LSTM神经网络根据之前时间段的步态序列有效地预测出下一时刻的步态信息，平均准确率能够达到92.9%以上。

Gait Prediction Method of Lower Extremity Exoskeleton Based on SAE and LSTM Neural Network

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%.