解析膝关节运动意图是实现下肢外骨骼机器人穿戴舒适性的核心.神经系统疾病患者常伴有下肢运动障碍,通过表面肌电信号对其进行运动评估.为实现上述患者在运动评估与关节角度预测的融合,本文提出一种新型的基于注意力机制的CNN-LSTM网络模型,通过10通道表面肌电信号实现水平行走、上坡和上楼梯时 3种日常运动膝关节角度预测,其预测误差指标均方根误差(Root mean square error,RMSE)、平均绝对误差(Mean absolute error,MAE)和决定系数(R2)均值分别为 2.74、2.50和 0.97,均优于传统网络模型.进一步,通过消融实验,显示上述 3个预测指标分别平均下降了20.47%、34.36%和6.59%.可见,本文提出的基于注意力机制的CNN-LSTM模型端到端预测方法具有最高的预测精度,为下肢外骨骼机器人系统的人机交互控制方案提供了参考.
An Attention Mechanism-Based CNN-LSTM Framework for Lower Limb Knee Joint Angle Prediction
Decoding knee motion intention is crucial for the wearable comfort in lower extremity exoskeleton robots.Patients with neurological disorders are often accompanied with lower limb movement disorders assessed by surface electromyography(sEMG)signals.To integrate the motion assessment and joint angle prediction for these patients,a novel CNN-LSTM framework based on the attention mechanism is proposed to predict the knee joint angle for three daily motions,i.e.,horizontal walking,going uphill,and going up stairs,through 10-channel sEMG signals.The prediction error indicators,i.e.,the root mean squared error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R2)reach 2.74,2.50,and 0.97,respectively,outperforming the traditional network.Furthermore,the ablation experiments show the three indicators have decreased by 20.47%,34.36%and 6.59%on average,respectively.The proposed end-to-end prediction framework based on the attention mechanism can reach the highest prediction accuracy,providing a reference for the human-robot interaction scheme of the lower limb exoskeleton robot system.
万方数据
维普
