ML-Based Classification of Hamstring Strain Injury from Nonlinear Features of Surface Electromyography Signals

Hamstring muscles are commonly injured in sports involving strenuous activities. Evaluating the muscle condition is done through physical examinations like ultrasonography and MRI, which provide accurate assessments but are costly. An alternative method in obtaining information from muscle activity is through Surface Electromyography (sEMG) signals, which is recorded through electrical signals. Particularly, the nonlinear features are assessed to monitor muscle activity in the hamstring since these can determine intrinsic changes in muscle behavior. These nonlinear features bring about high dimensionality to the signal, which requires manifold learning to reduce its dimensionality and extract its underlying nonlinear features and relationships. In this paper, the researchers propose a solution that provides an automated classification of HSI based on nonlinear features of sEMG signals using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LoR), along with manifold learning techniques such as t-distributed Stochastic Neighborhood Embedding (t-SNE) and Locally Linear Embedding (LLE). The study results showed that SVM was considered the best overall predictive model for classification, with an accuracy of 88.51% when applied on t-SNE and 75.40% when applied on LLE.