# SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning
## 4 Results and Analysis
### Dataset I  
Figure 4 shows that the SVSL method achieved an average accuracy of 99.26%, which is better than that of the other three classifiers, RF, GLM, DL, and AdaBoost, with 98.09%, 90.84%, 97.90%, and 96.13%, respectively. Furthermore, the SVSL method showed a 0.14% gain with the self-training mechanism, which indicates that the SVSL method can correctly classify the most challenging data instances. The normalized confusion matrices of the SVSL method, DL, GLM, RF, and AdaBoost are shown in Figures 5-9. Each confusion matrix contains the sum of the rightly classified and misclassified classes. The diagonal of the matrix represents the rightly classified class details. Figure 4: Prediction accuracy (%) of the SVSL method, RF, GLM, DL, and AdaBoost. Figure 5: Normalized confusion matrix for the deep learning (DL) algorithm. Figure 8: Normalized confusion matrix of the AdaBoost method. Figure 6: Normalized confusion matrix of the Generalized Linear Model (GLM). Figure 7: Normalized confusion matrix of Random Forest (RF). Figure 5 depicts the conventional and normalized confusion matrix for the deep-learning classifier, as we know that the matrix values in the figure can be used to compute the percentage values of the class-wise prediction accuracy. The deep-learning classifier produced the highest class-wise accuracy of 100% for the walking down the stairs and lying activities. The lowest accuracy, 93.9%, produced by the deep-learning classifier was for the standing class. The deep-learning classifier produced accuracies of 99.6%, 98.5%, 98.30%, and 96.1% for the other four activities--walking, walking up the stairs, walking down the stairs, and sitting, respectively. It should be noted that, as shown in Figure 4, the deep-learning classifier produced an average accuracy of 97.90% for all the classes. Figure 6 depicts the normalized confusion matrix for the GLM. Using the confusion matrix values in the figure to compute the percentage values of the class-wise prediction accuracy, we note that the GLM classifier produced the highest accuracy of 99.3% for the lying position. The lowest accuracy, 83.3%, was for the standing activity. The GLM classifier produced accuracies of 88.8%, 92.1%, 93.6%, and 88.4% for the other four activities--Walking Up the stairs, Walking, Walking Down the stairs, and Sitting, respectively. It should be noted that, as shown in Figure 4 the GLM classifier produced an average accuracy of 90.84% for all the classes. Figure 7 depicts the conventional and normalized confusion matrix for the RF classifier. Using the confusion matrix values in the figure to compute the percentage values of the class-wise prediction accuracy, we note that the RF classifier produced the highest accuracy of 100% for the lying position. The lowest accuracy, 94.7%, was for the standing activity. The RF classifier produced accuracies of 98.5%, 99.2%, 99.5%, and 97.1% for the other four activities--walking up the stairs, walking, walking down the stairs, and sitting, respectively. It should be noted that, as shown in Figure 4, the RF classifier produced an average accuracy of 98.09% for all the classes. Figure 8 depicts the normalized confusion matrix for the AdaBoost classifier. Using the confusion matrix values in the figure to compute the percentage values of the class-wise prediction accuracy, we note that the AdaBoost classifier produced the highest accuracy of 99.4% for the lying position. The lowest accuracy, 90.9%, was for the standing activity. The AdaBoost classifier had accuracies of 98.2%, 97.6%, 98.5%, and 92.2% for the other four activities--Walking Up the stairs, Walking, Walking Down the stairs, and Sitting, respectively. It should be noted that, as shown in Figure 4, the AdaBoost classifier produced an average accuracy of 96.13% for all the classes. Figure 9 depicts the conventional and normalized confusion matrix for the SVSL method. Using the confusion matrix values in the figure to compute the percentage values  
Figure 9: Normalized confusion matrix of the SVSL method.