Knee joint vibration signal classification algorithm based on machine learning
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S.I. : ATCI 2020
Knee joint vibration signal classification algorithm based on machine learning Yi Zheng1,2 • Youqiang Wang2 • Jixin Liu1 • Haiyan Jiang3 • Qingchao Yue1 Received: 19 May 2020 / Accepted: 16 September 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Machine learning Knee joint vibration signal (VAG signal) Classification algorithm
1 The importance of the knee joint
& Youqiang Wang [email protected] Yi Zheng [email protected] Jixin Liu [email protected] Haiyan Jiang [email protected] Qingchao Yue [email protected] 1
Institute of Intelligence and Manufacture, Qingdao Huanghai University, Qingdao 266427, China
2
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
3
Department of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Tai’an 271000, China
In people’s daily activities, the knee joints almost bear the full weight of the body. Once the activity is wrong, it is easily damaged or has lesions. Common knee injuries and diseases include cruciate ligament injury, meniscus injury, articular cartilage degradation, and osteoarthritis. The early diagnosis and treatment of knee injury and disease are of great significance to the protection of the health of the contemporary population. The knee joint vibration signal is defined as the vibration signal generated by the knee joint during the stretching or bending motion. By analyzing the characteristics of the knee joint vibration signal and using the classification method such as machine learning, it is possible to effectively distinguish the normal and abnormal knee joint vibration signals related to pathology. This kind of examination can detect
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