基于LSTM和Conformer的下肢外骨骼步态预测方法  

Gait prediction method of lower limb exoskeleton based on LSTM and Conformer neural network

作　　者：赵侦钧 王涛[1] 贝太学 宋涛涛 Zhao Zhenjun;Wang Tao;Bei Taixue;Song Taotao(School of Information and Electrical Engineering,Shandong Jianzhu University,Jinan,Shandong 250000,China;School of Mechanical and Electrical Engineering,Shandong Jianzhu University)

机构地区：[1]山东建筑大学信息与电气工程学院,山东济南250000 [2]山东建筑大学机电工程学院

出　　处：《计算机时代》2022年第8期1-5,共5页Computer Era

基　　金：山东省重大科技创新工程项目(2018YFJH0306);山东省重点研发计划项目(2019GGX104056);山东建筑大学博士科研基金(X18067Z)。

摘　　要：提出一种新颖的基于长短期记忆神经网络(Long Short-term Memory,LSTM)和Conformer相结合的步态预测方法,用于解决下肢外骨骼人机协同问题。首先利用LSTM网络模型在时间上对步态数据序列做初步的特征提取及预测,然后采用Conformer模型对LSTM模型输出的数据在时空上作进一步的深度特征提取,并经线性激活单元输出预测结果。利用Pytorch搭建LSTM-Conformer神经网络模型,由采集到的下肢姿态数据组建成的数据集作为输入,将步态所属类别标签作为输出进行验证。实验结果表明,拟议网络模型平均准确率达到了94.89%。A novel gait prediction method based on Long Short-Term Memory(LSTM)and Conformer is proposed to solve the problem of human-machine coordination of lower limb exoskeleton.Firstly,the LSTM network model is used to perform preliminary feature extraction and prediction on the gait data sequence in time,and then the Conformer model is used to perform further in-depth feature extraction on the LSTM model output data in time and space,and the prediction results are output through the linear activation unit.The LSTM-Conformer neural network model is built on the Pytorch.The data set constructed from the collected lower limb posture data group is used as input,and the category label of the gait is used as the output for verification.The experimental results show that the average accuracy rate of the proposed network model reaches 94.89%.

关 键 词：外骨骼 步态预测 九轴姿态传感器 长短期记忆网络 Conformer模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]