Abstract
The knee joint is the largest and most complex flexion and extension joint of the human body. It supports most of the weight of the human body during the whole body during standing or exercise. Because the knee joint has the characteristics of complex structure and large load, it is also vulnerable to damage. An effective diagnosis in the early stage of injury or lesion of the knee joint is of great help to the later treatment. At present, the commonly used knee joint examination methods have the problems of large trauma and high cost. Therefore, this paper uses machine learning technology to study the classification algorithm of knee joint vibration signal. The research results of this paper were verified by selecting the subjects to form a healthy group and a disease injury group. The experimental results show that the proposed signal denoising algorithm is superior to the traditional denoising algorithm. After analyzing several classification algorithms, the multi-classifier fusion algorithm has excellent performance in signal classification. The experimental results show that the research results can be applied to the classification of knee joint vibration signals, and then applied to the clinical diagnosis of knee joint diseases.
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Acknowledgements
This work was supported by Shandong Key Research and Development Project (No.2019GHY112068;No.2019GGX101011), SDUST Research Fund (No. 2018TDJH101), Open Fund Project of Shandong Province Key Laboratory of Mine Mechanical Engineering (2019KLMM202), and Shandong Provincial College Science and Technology Planning project (J18KA009).
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Zheng, Y., Wang, Y., Liu, J. et al. Knee joint vibration signal classification algorithm based on machine learning. Neural Comput & Applic 33, 985–995 (2021). https://doi.org/10.1007/s00521-020-05370-z
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DOI: https://doi.org/10.1007/s00521-020-05370-z