Jin, XinGuo, JiaLi, ZhongWang, Ruihao2021-01-042021-01-042020-12-22Xin Jin, Jia Guo, Zhong Li, and Ruihao Wang, “Motion Prediction of Human Wearing Powered Exoskeleton,” Mathematical Problems in Engineering, vol. 2020, Article ID 8899880, 8 pages, 2020. doi:10.1155/2020/8899880http://hdl.handle.net/10919/101716With the development of powered exoskeleton in recent years, one important limitation is the capability of collaborating with human. Human-machine interaction requires the exoskeleton to accurately predict the human motion of the upcoming movement. Many recent works implement neural network algorithms such as recurrent neural networks (RNN) in motion prediction. However, they are still insufficient in efficiency and accuracy. In this paper, a Gaussian process latent variable model (GPLVM) is employed to transform the high-dimensional data into low-dimensional data. Combining with the nonlinear autoregressive (NAR) neural network, the GPLVM-NAR method is proposed to predict human motions. Experiments with volunteers wearing powered exoskeleton performing different types of motion are conducted. Results validate that the proposed method can forecast the future human motion with relative error of 2%∼5% and average calculation time of 120 s∼155 s, depending on the type of different motions.application/pdfenCreative Commons Attribution 4.0 InternationalArticle - Refereed2020-12-27Copyright © 2020 Xin Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.https://doi.org/10.1155/2020/8899880