Abstract
In the past decade, wearable inertial sensor based human activity recognition has attracted lots of attention from researchers in the world. High-dimensional feature set will increase the computation and memory cost. In this paper, kernel PCA has been utilized for dimensionality reduction to deal with inertial sensor based human activity recognition. However, kernel method may increase the computation and memory cost. Thus, reduced kernel method is proposed. The real dataset has been utilized to evaluate the proposed reduced kernel PCA (RKPCA) method. Experimental results demonstrate the efficacy of the proposed method, which achieves better results than traditional PCA method.
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Acknowledgements
This work was supported by Fundamental Research in cutting-edge technologies in the project of Henan province (162300410070), Key Science Technology Program of Henan Province, China (19A413013), Foundation of Henan Educational Committee (162300410070), and Ph.D. early development program of Zhengzhou University of Light Industry (13501050009).
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Wu, D., Zhang, H., Niu, C., Ren, J., Zhao, W. (2019). Inertial Sensor Based Human Activity Recognition via Reduced Kernel PCA. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_34
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DOI: https://doi.org/10.1007/978-3-030-02819-0_34
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