Muscle synergy reflects inherent coordination patterns of muscle groups as the human body finishes required movements. It may be still unknown whether the original muscle synergy of subjects may alter or not when exoskeletons are put on during their normal walking activities. This paper reports experimental results and presents analysis on muscle synergy from 17 able-bodied subjects with and without lower-limb exoskeletons when they performed normal walking tasks. The electromyography (EMG) signals of the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (GAS), vastus medialis oblique (VMO), vastus lateralis oblique (VLO), biceps femoris (BICE), semitendinosus (SEMI), and rectus femoris (RECT) muscles were extracted to obtain the muscle synergy. The quantitative results show that, when the subjects wore exoskeletons to walk normally, their mean muscle synergy changed from when they walked without exoskeletons. When the subjects walked with and without exoskeletons, statistically significant differences on sub-patterns of the muscles' synergies between the corresponding two groups could be found.

As exoskeleton robots are more frequently applied to impaired people to regain mobility, detection and recognition of human gait motions is important to prepare suitable control modes for exoskeletons. This paper proposes to explore the potential of the ensemble empirical mode decomposition (EEMD) method to help analyze and recognize gait motions for human subjects who wear the exoskeleton to walk. The intrinsic mode functions (IMFs) extracted from the original gait signals by EEMD are utilized to act as inputs for classification algorithms. Evident correlations are found between some IMFs and original gait kinematic sequences. Experimental results on gait phase recognition performance on 14 able-bodied subjects are shown. The performance of the composing signals extracted from the original signals as IMF 1 ∼ IMF 8 is investigated, which indicates that IMF 8 might be helpful when wearing exoskeleton and IMF 5 might be helpful when walking without exoskeleton on gait recognition. And the similarity of joint synergy between wearing and without wearing exoskeleton is analyzed, and the result shows that the joint synergy might change between with and without wearing exoskeleton. The quantitative results show that based on some IMFs of the same orders, these machine learning algorithms can achieve promising performances.