School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 451191, China.
Research Organization of Science and Technology, Ritsumeikan University, Kusatsu 525-8577, Japan.
Sensors (Basel). 2023 Jul 20;23(14):6547. doi: 10.3390/s23146547.
Lower limb exoskeleton robots have shown significant research value due to their capabilities of providing assistance to wearers and improving physical motion functions. As a type of robotic technology, wearable robots are directly in contact with the wearer's limbs during operation, necessitating a high level of human-robot collaboration to ensure safety and efficacy. Furthermore, gait prediction for the wearer, which helps to compensate for sensor delays and provide references for controller design, is crucial for improving the the human-robot collaboration capability. For gait prediction, the plantar force intrinsically reflects crucial gait patterns regardless of individual differences. To be exact, the plantar force encompasses a doubled three-axis force, which varies over time concerning the two feet, which also reflects the gait patterns indistinctly. In this paper, we developed a transformer-based neural network (TFSformer) comprising convolution and variational mode decomposition (VMD) to predict bilateral hip and knee joint angles utilizing the plantar pressure. Given the distinct information contained in the temporal and the force-space dimensions of plantar pressure, the encoder uses 1D convolution to obtain the integrated features in the two dimensions. As for the decoder, it utilizes a multi-channel attention mechanism to simultaneously focus on both dimensions and a deep multi-channel attention structure to reduce the computational and memory consumption. Furthermore, VMD is applied to networks to better distinguish the trends and changes in data. The model is trained and tested on a self-constructed dataset that consists of data from 35 volunteers. The experimental results show that FTSformer reduces the mean absolute error (MAE) up to 10.83%, 15.04% and 8.05% and the mean squared error (MSE) by 20.40%, 29.90% and 12.60% compared to the CNN model, the transformer model and the CNN transformer model, respectively.
下肢外骨骼机器人因其为佩戴者提供辅助和改善身体运动功能的能力而显示出重要的研究价值。作为一种机器人技术，可穿戴机器人在操作过程中直接与佩戴者的四肢接触，因此需要高度的人机协作，以确保安全和有效。此外，对佩戴者的步态预测有助于补偿传感器延迟并为控制器设计提供参考，对于提高人机协作能力至关重要。对于步态预测，足底力本质上反映了关键的步态模式，而与个体差异无关。确切地说，足底力包含一个双倍的三轴力，它随时间变化，涉及双脚，也能反映出步态模式的不同。在本文中，我们开发了一种基于变压器的神经网络（TFSformer），该网络结合了卷积和变分模态分解（VMD），利用足底压力预测双侧髋关节和膝关节角度。考虑到足底压力在时间和力空间维度上包含的不同信息，编码器使用 1D 卷积来获取这两个维度的综合特征。对于解码器，它利用多通道注意力机制同时关注两个维度，并利用深多通道注意力结构来减少计算和内存消耗。此外，VMD 应用于网络中，以更好地区分数据的趋势和变化。该模型在一个自建的数据集上进行训练和测试，该数据集由 35 名志愿者的数据组成。实验结果表明，与 CNN 模型、Transformer 模型和 CNN-Transformer 模型相比，TFSformer 将平均绝对误差（MAE）降低了 10.83%、15.04%和 8.05%，将平均平方误差（MSE）降低了 20.40%、29.90%和 12.60%。
