Abstract
Powered hip exoskeletons have shown the ability for locomotion assistance during treadmill walking. However, providing suitable assistance in real-world walking scenarios which involve changing terrain remains challenging. Recent research suggests that forecasting the lower limb joint's angles could provide target trajectories for exoskeletons and prostheses, and the performance could be improved with visual information. In this letter, We share a real-world dataset of 10 healthy subjects walking through five common types of terrain with stride-level label. We design a network called Sandwich Fusion Transformer for Image and Kinematics (SFTIK), which predicts the thigh angle of the ensuing stride given the terrain images at the beginning of the preceding and the ensuing stride and the IMU time series during the preceding stride. We introduce width-level patchify, tailored for egocentric terrain images, to reduce the computational demands. We demonstrate the proposed sandwich input and fusion mechanism could significantly improve the forecasting performance. Overall, the SFTIK outperforms baseline methods, achieving a computational efficiency of 3.31 G Flops, and root mean square error (RMSE) of 3.445 \textpm \ 0.804\textdegree \ and Pearson's correlation coefficient (PCC) of 0.971 \textpm\ 0.025. The results demonstrate that SFTIK could forecast the thigh's angle accurately with low computational cost, which could serve as a terrain adaptive trajectory planning method for hip exoskeletons. Codes and data are available at this https URL.
Abstract (translated)
电动髋外骨骼装置在踏步机步行过程中表现出协助运动的能力。然而，在现实世界中涉及改变地形的情景提供适当的协助仍然具有挑战性。最近的研究表明，预测下肢关节的角度可能为外骨骼和假肢提供目标轨迹，并且可以通过视觉信息提高性能。在这封信中，我们分享了由10名健康受试者组成的真实世界数据集，他们在五种常见的地形上行走，包括水平带标签。我们设计了一个名为Sandwich Fusion Transformer for Image and Kinematics (SFTIK)的图像和运动预测网络，该网络在先前和后续步道的地形图像上预测随后的步道大腿角度，以及前一步的时间序列中的IMU数据。我们还引入了宽度级别的补全，专门针对以自旋为中心的地形图像，以降低计算需求。我们证明了所提出的sandwich输入和融合机制可以显著提高预测性能。总体而言，SFTIK超越了基线方法，实现3.31 G Flops的计算效率和3.445 \textpm \ 0.804\textdegree \的 root mean square error (RMSE) 和0.971 \textpm\ 0.025的 Pearson's correlation coefficient (PCC)。结果表明，SFTIK可以在低计算成本下准确预测大腿的角度，这可以为髋外骨骼器提供地形自适应轨迹规划方法。代码和数据可在此https URL获取。
URL
https://arxiv.org/abs/2404.11945
