## 3 Results
### Performance Metrics  
Different evaluation metrics like accuracy, precision, recall, and F1 score were computed using the obtained confusion matrices as shown in Figure 10A, and the corresponding \(p\)-value obtained from the \(t\)-Test between the test accuracies and cross-validation accuracies for each classifier to check the overfitting amongst the classifiers is shown in Figure 10B. Decision Tree, Random Forest, Gradient Boosting, Multilayer Perceptron, and Bagging classifiers achieved maximum accuracy, precision, recall, and F1 score, whereas the Gaussian  
Figure 7: EMG acquisition and power spectral analysis: **(A)** The electrode placement at the vastus lateralis muscle to acquire EMG is illustrated. The ground electrode is placed near the ankle of the subject. **(B)** A small chunk of the acquired EMG signal is plotted. There is time in seconds on the x-axis and amplitude in millivols on the y-axis. **(C)** The plot shows the power spectrum and its density mapped against the frequency values of the entire EMG signal. **(D)** This is a fast Fourier transform plot of the entire EMG signal showing the frequency components in the EMG signal and their magnitudes. **(E)** Short-Time Fourier Transform of the small chunk of EMG signal is illustrated in the figure. It has frequency and power spectral density plotted against the time in seconds. The high-power spectral density during the action of the vastus lateralis muscle is shown in red color and the low density is shown in blue color. **(F)** A plot without color mapping is shown in the figure, having the values of power per frequency component plotted against the frequency.  
Naive Bayes classifier achieved minimum accuracy, precision, recall, and F1 score amongst all other classifiers. The accuracy shown in this graph is without the k-fold cross-validation. A \(p\) value less than 0.05 is the indicator of potential overfitting. The \(p\)-value in the case of Random Forest is less than 0.05, and hence there is overfitting in that case, whereas there is no overfitting in the case of other classifiers. All the classifiers except Random Forest performed exceptionally well in the classification of body imbalance and its intensities into low, medium, and high.  
Figure 8: Average 10-fold cross validation accuracies and error bars: (**A**) Average cross-validation accuracies of machine learning classifiers are illustrated in a bar chart. The highest 10-fold cross-validation accuracies are achieved by Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and Bagging classifiers. (**B**) Average cross-validation accuracies are illustrated along with their error bars. Error is negligible in the case of Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and Bagging classifiers.  
Figure 9: Confusion matrices obtained after classification: Confusion matrices are illustrated in **(A–H)** subplots for different machine learning classifiers like k-Nearest Neighbors, Linear Discriminant Analysis, Gaussian Naive Bayes, Decision Tree, Gradient Boosting, Multilayer Perceptron, AdaBoost and Bagging classifiers, respectively. The diagonal boxes in shades of dark and light blue color represent true values predicted correctly by the classifiers corresponding to the high, medium, and low classes.