Researchers have made advances in reducing the metabolic rate of both walking and running by modulating mono-articular energy with exoskeletons. However, how to modulate multiarticular energy with exoskeletons to improve the energy economy of both walking and running is still a challenging problem, due to the lack of understanding of energy transfer among human lower-limb joints. Based on the study of the energy recycling and energy transfer function of biarticular muscles, we proposed a hip–knee unpowered exoskeleton that emulates and reinforces the function of the hamstrings and rectus femoris in different gait phases. The biarticular exo-tendon of the exoskeleton assists hamstrings to recycle the kinetic energy of the leg swing while providing hip extension torque in the swing phase. In the following stance phase, the exo-tendon releases the stored energy to assist the co-contraction of gluteus maximus and rectus femoris for both hip extension and knee extension, thus realizing the phased modulation of hip and knee joint energy. The metabolic rate of both walking (1.5 m/s) and running (2.5 m/s) can be reduced by 6.2% and 4.0% with the multiarticular energy modulation of a hip–knee unpowered exoskeleton, compared to that of walking and running without an exoskeleton. The bio-inspired design method of this study may inspire people to develop devices that assist multiple gaits in the future.

In particle image velocimetry (PIV) experiments, background noise inevitably exists in the particle images when a particle image is being captured or transmitted, which blurs the particle image, reduces the information entropy of the image, and finally makes the obtained flow field inaccurate. Taking a low-quality original particle image as the research object in this research, a frequency domain processing method based on wavelet decomposition and reconstruction was applied to perform particle image pre-processing. Information entropy analysis was used to evaluate the effect of image processing. The results showed that useful high-frequency particle information representing particle image details in the original particle image was effectively extracted and enhanced, and the image background noise was significantly weakened. Then, information entropy analysis of the image revealed that compared with the unprocessed original particle image, the reconstructed particle image contained more effective details of the particles with higher information entropy. Based on reconstructed particle images, a more accurate flow field can be obtained within a lower error range.

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