摘要
利用一种基于通道注意力机制增强的有向图神经网络(Channel Attention Enhanced Directed Graph Neural Network,CA-DGNN)的外骨骼机器人步态相位预测方法,提高了步态相位预测的准确性和可靠性。首先,研制了人体下肢姿态信息采集装置,采集人体下肢的行走步态数据并构建人体下肢的骨架模型;之后,建立了基于CA-DGNN步态相位的预测模型,提取人体步态相位的运动特征,并基于当前时刻数据预测未来时刻的步态相位;最后,探讨了滑动窗口大小对算法性能的影响。本文提高了外骨骼机器人步态相位预测的准确性和鲁棒性,为此方向研究提供了一种新的思路和方法。
Gait phase prediction holds significant importance in the control of assistive robotic devices,such as exoskeletons.The control unit is required to discern the gait phase to supply the necessary power during operation.Given that current gait phase prediction methods based on the Inertial Measurement Unit(IMU)do not fully leverage the relationship between joints and bones,this study presents a gait phase prediction approach for an exoskeleton robot using a Channel Attention-enhanced Directed Graph Neural Network(CA-DGNN)to enhance prediction accuracy and reliability.Initially,a device for collecting human lower limb posture information is developed to gather walking gait data and construct a skeleton model of the lower limbs.Subsequently,a gait phase prediction model based on CA-DGNN is established to extract motion characteristics of human gait phases and predict the gait phase at a future moment based on current data.Lastly,the impact of the sliding window size on the algorithm's performance is analyzed.The experimental results show that compared to other algorithms,the prediction accuracy of CA-DGNN is 97.88%,which is better than other four algorithms such as CNN,RNN,TCN and LSTM.This work aims to present an innovative idea and method for gait phase prediction in exoskeleton robots,thereby advancing the accuracy and robustness in such robotic systems.
作者
颜建军
许赢家
林越
金理
江金林
YAN Jianjun;XU Yingjia;LIN Yue;JIN Li;JIANG Jinlin(School of Mechanical and Power Engineering,East China University of Science and Technology,Shanghai 200237,China;Shanghai Aerospace Control Technology Institute,Shanghai 200235,China)
出处
《华东理工大学学报(自然科学版)》
北大核心
2025年第1期110-118,共9页
Journal of East China University of Science and Technology
基金
国家自然科学基金重大研究计划(91748110)。
关键词
步态相位预测
惯性传感器
骨架
时空图卷积网络
通道注意力机制
gait phase prediction
IMU
skeleton
spatial temporal graph convolutional networks
channel attention mechanism
