Abstract
Dynamics of human wrist play an important role in human-robot interaction. In this paper, we develop a novel method to classify the human wrist’s motion and to recognize its stiffness profile. In the proposed method, an integrated framework of linear discriminant analysis and extreme learning machine is developed to evaluate the intention of the wrist. Specifically, linear discriminant analysis is used to classify gestures of the wrist. Based on the result of classification, extreme learning method is use to construct a regression model of the stiffness. The experimental results are demonstrated the effectiveness of the proposed method.
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Funding
This work was partially supported by National Nature Science Foundation (NSFC) under Grants 61861136009 and 61811530281 Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/S001913.
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Luo, J., Liu, C., Wang, N., Yang, C. (2020). A Method of Intention Estimation for Human-Robot Interaction. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_6
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DOI: https://doi.org/10.1007/978-3-030-29933-0_6
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