This current work describes human push recovery data classification using features that are obtained from intrinsic mode functions by performing empirical mode decomposition on different leg joint angles (hip, knee and ankle). Joint angle data were calculated for both open-eyes and closed-eyes subjects. Four kinds of pushes were applied (small, medium, moderately high, high) during the experiment to analyze the recovery mechanism. The classification was performed based on these different kinds of the pushes using deep neural network (DNN), and 89.28 % overall accuracy was achieved. The first classifier was based on artificial neural network on feed-forward back-propagation neural network (FF-BPNN), and second one was based on DNN. The proposed DNN-based classifier has been applied and evaluated on four types of pushes, i.e., small, medium, moderately high, high. The classification accuracy with a success of 88.4 % has been obtained using fivefold cross-validation approach. The analysis of variance has also been conducted to show the statistical significance of results. The corresponding strategies (hip, knee, and ankle) can be utilized once the categories of pushes (small, medium, moderately high, high) were identified accordingly push recovery (Semwal et al. in International conference on control, automation, robotics and embedded systems (CARE), pp 1–6, 2013).
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Semwal VB, BhushanA, Nandi GC (2013) Study of humanoid Push recovery based on experiments. In: International conference on control, automation, robotics and embedded systems (CARE), pp 1–6
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The authors would like to thank all the research scholars, M.Tech. students and technical staffs for their comments and suggestions. At the same time, our sincere thanks to IIIT, Allahabad, for providing us all the necessary facilities for research.
An erratum to this article is available at http://dx.doi.org/10.1007/s00521-017-3029-1.
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Semwal, V.B., Mondal, K. & Nandi, G.C. Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput & Applic 28, 565–574 (2017). https://doi.org/10.1007/s00521-015-2089-3
- Push recovery
- Feature selection
- Fivefold cross-validation