Real-Time Intended Knee Joint Motion Prediction by Deep-Recurrent Neural Networks
Real-Time Intended Knee Joint Motion Prediction by Deep-Recurrent Neural Networks
复制标题
通过深度循环神经网络进行实时预期膝关节运动预测
DOI：
10.1109/jsen.2019.2933603
复制
发表时间：
2019-12-01
影响因子：
4.3
通讯作者：
Liu, Tao
中科院分区：
文献类型：
--
作者：
Huang, Yongchuang;He, Zexia;Liu, Tao
人工辅助智能系统需要一定的方法来精确预测机动肢体关节角度。介绍了一种用于处理序列数据的神经网络--深度递归神经网络(RNN)在膝关节角度实时预测中的应用。这个模型是基于肌电(EMG)信号(将电极放置在三块腿部肌肉上)和大腿和小腿的惯性测量的组合而创建的。使用不同受试者在执行不同步态时收集的数据来构建模型，并在实时环境中进行评估。提出的基于融合信息的神经网络模型在计算复杂度和预测精度之间取得了平衡。在微控制器上的结果表明，在50ms的预测范围内，该模型的预测误差为±2.93度。
Human-assisting intelligent systems demand certain methods to precisely predict motorized limb joint angles. This paper presents the application of deep-recurrent neural networks (RNNs), which is a type of neural network for processing sequential data, for predicting the knee joint angle in real-time. This model is created based on a combination of electromyographic (EMG) signals, (with electrodes being placed on three leg muscles), and inertial measurements of the upper and lower legs. The data collected from different subjects when they performed different gaits were used to construct the model, which was evaluated in a real-time setting. The proposed RNN model based on fusion information contains a balance between computational complexity and prediction accuracy. Results on a microcontroller show that, within a predicted horizon of 50 ms, the model has a low prediction error of ±2.93 degrees.