Integrating frequency and dynamic characteristics of EMG signals as a new inter-muscular coordination feature.

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.