Abstract
The application of medical big data and artificial intelligence algorithms are majorly popular in biomedical field. In this paper, BP neural network optimized by genetic algorithm was used to study the classification of muscle fatigue. Although BP neural network has a strong nonlinear mapping ability by using the gradient descent search method, it is easy to fall into the local minimum during the search process because of the randomness of the initial weights and thresholds generated, which would affect the training rate and the accuracy of muscle fatigue classification. the genetic algorithm was used to complete the configuration of the initial population parameters and the design of fitness function, and the optimal weights and thresholds that met the conditions were output to BP neural network. Finally, the classification results of muscle fatigue were output. The experimental results showed that the GA-BP neural network had a stronger ability to jump out of the local optimization compared with the classification effect of BP neural network. The maximum recognition rate of fatigue state reached 90.4%, and the model running time was 17.1 s, which was relatively reduced by 4.5 s.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, J., Sun, Y., Sun, S.: Recognition of muscle fatigue status based on improved wavelet threshold and CNN-SVM. IEEE Access 8, 207914–207922 (2020)
Veiga, J., Faria, R.C., Esteves, G.P., et al.: Approximate entropy as a measure of the airflow pattern complexity in asthma. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010, 2463–2466 (2010)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. U S A. 88(6), 2297–2301 (1991)
Li, H., Wang, Q., Liu, J., Zhao, D.: A prediction model of human resources recruitment demand based on convolutional collaborative BP neural network. Comput. Intell. Neurosci. 24(2022), 3620312 (2022)
Moniri, A., Terracina, D., Rodriguez-Manzano, J., et al.: Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning. IEEE Trans. Biomed. Eng. 68(2), 718–727 (2021)
Zhao, G., Shi, H., Wang, J.: A grey BP neural network-based model for prediction of court decision service rate. Comput. Intell. Neurosci. 14, 7364375 (2022)
Zhou, L., Wang, C.: Innovation of platform economy business model driven by BP neural network and artificial intelligence technology. Comput. Intell. Neurosci. 9(2022), 3467773 (2022)
Wang, Z., Wu, J., Wang, H., Wang, H., Hao, Y.: Optimal underwater acoustic warfare strategy based on a three-layer GA-BP neural network. Sensors (Basel) 22(24), 9701 (2022)
Ning, Y., Jin, Y., Peng, Y., Yan, J.: Small obstacle size prediction based on a GA-BP neural network. Appl. Opt. 61(1), 177–187 (2022)
Wang, L., Qiu, K., Li, W.: Sports action recognition based on GB-BP neural network and big data analysis. Comput. Intell. Neurosci. 2(2021), 1678123 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zang, M., Xing, L., Qian, Z., Yao, L. (2023). Muscle Fatigue Classification Based on GA Optimization of BP Neural Network. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_23
Download citation
DOI: https://doi.org/10.1007/978-981-99-3300-6_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3299-3
Online ISBN: 978-981-99-3300-6
eBook Packages: Computer ScienceComputer Science (R0)