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Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

Jianguo Wang1, Kuan Zhang1,*, Yuesheng Zhao2,*, Xiaoling Wang2, Muhammad Shamrooz Aslam2

1 School of Biomedical Engineering, Capital Medical University, Beijing, 100054, China
2 School of Automation, Guangxi University of Science and Technology, Liuzhou, 545006, China

* Corresponding Authors: Kuan Zhang. Email: email; Yuesheng Zhao. Email: email

Computers, Materials & Continua 2023, 77(1), 79-96. https://doi.org/10.32604/cmc.2023.040506

Abstract

The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm, etc. Thus, a solution is presented under the guidance of right prior knowledge, using Back-Propagation neural networks (BP networks) and simple Convolutional Neural Networks (CNN). The results show that HAR can be achieved with 90%–100% accuracy. Further analysis shows that intelligent algorithms for pattern recognition and classification problems, typically represented by HAR, require correct prior knowledge to work effectively.

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Cite This Article

APA Style
Wang, J., Zhang, K., Zhao, Y., Wang, X., Aslam, M.S. (2023). Cascade human activity recognition based on simple computations incorporating appropriate prior knowledge. Computers, Materials & Continua, 77(1), 79-96. https://doi.org/10.32604/cmc.2023.040506
Vancouver Style
Wang J, Zhang K, Zhao Y, Wang X, Aslam MS. Cascade human activity recognition based on simple computations incorporating appropriate prior knowledge. Comput Mater Contin. 2023;77(1):79-96 https://doi.org/10.32604/cmc.2023.040506
IEEE Style
J. Wang, K. Zhang, Y. Zhao, X. Wang, and M.S. Aslam, “Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge,” Comput. Mater. Contin., vol. 77, no. 1, pp. 79-96, 2023. https://doi.org/10.32604/cmc.2023.040506



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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