Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition

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 is investigated, which indicates that might be helpful when wearing exoskeleton and 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.