Khanmiri Shaghayegh Hassanzadeh, Ghaderyan Peyvand, Oskouei Alireza Hashemi
Computational NeuroCognitive Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran.
Phys Eng Sci Med. 2025 Aug 21. doi: 10.1007/s13246-025-01620-3.
The impairment of inter-muscular coordination and changes in frequency components are two major pathological symptoms associated with knee injuries; however, an effective method to simultaneously quantify these changes has yet to be developed. Moreover, there is a need to propose a reliable automated system for identifying knee injuries to eliminate human errors and enhance reliability and consistency. Hence, this study introduces two novel inter-muscular coordination features: Dynamic Time Warping (DTW) and Dynamic Frequency Warping (DFW), which integrate time and frequency characteristics with a dynamic matching procedure. The support vector machine classifier and two types of dynamic neural network classifiers have also been used to evaluate the effectiveness of the proposed features. The proposed system has been tested using a public dataset that includes five channels of electromyogram (EMG) signals from 33 uninjured subjects and 28 individuals with various types of knee injuries. The experimental results have demonstrated the superiority of DFW and cascade forward neural network, achieving an accuracy rate of 92.03% for detection and 94.42% for categorization of different types of knee injuries. The reliability of the proposed feature has been confirmed in identifying knee injuries using both inter-limb and intra-limb EMG channels. This highlights the potential to offer a trade-off between high detection performance and cost-effective procedures by utilizing fewer channels.
肌肉间协调性受损和频率成分变化是与膝关节损伤相关的两个主要病理症状；然而，尚未开发出一种能同时量化这些变化的有效方法。此外，需要提出一种可靠的自动系统来识别膝关节损伤，以消除人为误差并提高可靠性和一致性。因此，本研究引入了两种新颖的肌肉间协调性特征：动态时间规整（DTW）和动态频率规整（DFW），它们通过动态匹配过程整合了时间和频率特征。还使用了支持向量机分类器和两种类型的动态神经网络分类器来评估所提出特征的有效性。所提出的系统已使用一个公共数据集进行测试，该数据集包括来自33名未受伤受试者和28名患有各种类型膝关节损伤个体的五通道肌电图（EMG）信号。实验结果证明了DFW和级联前馈神经网络的优越性，在检测不同类型膝关节损伤时准确率达到92.03%，在分类时准确率达到94.42%。所提出特征在使用肢体间和肢体内EMG通道识别膝关节损伤方面的可靠性得到了证实。这突出了通过使用较少通道在高检测性能和经济高效的程序之间进行权衡的潜力。
