## 4 Discussion  
Body imbalance contributes significantly to the variation in the patterns of both the GRF and EMG. Whenever body imbalance is induced, maintaining the center of gravity as well as the activation of muscles responsible for the stabilization of the body with respect to the center of gravity is crucial in avoiding a fall. The vastus lateralis muscle significantly contributes to balancing the body as it was observed that the vastus lateralis muscle had  
Figure 10: Evaluation metrics of classifiers upon classification: (**A**) Different performance metrics like accuracy, precision, recall, and F1 score are shown. The highest values of accuracy, precision, recall, and F1 score are achieved by Decision Tree, Random Forest, Gradient Boosting, Multilayer Perceptron, and Bagging classifiers, respectively, while they are lowest amongst others in the case of the Gaussian Naïve Bayes classifier. (**B**) There is overfitting in the case of the Random Forest classifier because it has a \(p\)-value less than 0.05 upon conducting a statistical test called the \(t\)-Test. the maximum amplitude of EMG compared to the other muscles of the lower limb, like the gastrocnemius, tibialis anterior, and peroneus muscles, as shown in Figure 7. Oculus Quest 2 was used to induce body imbalance effectively by creating virtual reality immersion using a game called _Epic Roller Coasters_ (Rock Falls Mode), as shown in Figure 4. The variations in GRF and EMG signals occur simultaneously, as shown in Figure 5. Using GRF and EMG to signals simultaneously forms a firm basis to classify imbalance intensities into high, medium, and low. Both normal and 10-fold cross-validation evaluation metrics showed the effectiveness of the machine learning classifiers as shown in Figures 8-10. Upon 10-fold cross validation, it has been observed that the standard deviation shown using the error bar plot in Figure 8B is minimum for Decision Tree, Random Forest, Gradient Boosting, and Bagging classifiers. These classifiers achieved the highest average 10-fold cross validation accuracies. The performance metrics were evaluated using the obtained confusion matrices. The values of true positives, true negatives, false positives, and false negatives from confusion matrices were used to compute accuracy, precision, recall and F1 score for all machine learning classifiers, as shown in Figures 9 and 10. Higher accuracy does not always indicate optimal performance of any classifier. Some classifiers tend to overfit the data, and when tested on unseen data, their performance drops and they fail perform well in classification. For this purpose, we must check the classifiers for potential overfitting of the data. One of the ways to test the overfitting of the data is to perform a statistical test named the \(t\)-Test. This test can be performed between training and test accuracies obtained from the classification of a machine learning classifier. If the \(p\)-value after conducting the \(t\)-Test is less than 0.05, this indicates that the classifier is overfitting the data. As shown in Figure 10, only the \(p\)-value for the Random Forest classifier is less than 0.05, which means the Random Forest classifier is overfitting the data. This study is only limited to muscles of the lower limb, whereas various muscles of the back can also be used to classify body imbalance and its intensities. Also, this study was conducted on only six healthy and young male subjects. It can also be conducted on both male and female healthy subjects as well as elderly subjects suffering from body imbalance impairments. Different virtual reality immersion environments can be tested to further optimize this work.