  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 https://github.com/RuoqiZhao116/SFTIK.
