## 4 Experimental Results and Discussion
### Discussion  
In this section, the approach for human activities using a granular computing classifier is evaluated versus traditional machine learning classifiers, i.e., KNN, Naive Bayes, MLP, Random Forest, and SVM. The granular computing classifier outperformed traditional machine learning classifiers. The evaluating metrics that are used in making comparisons include precision [38], recall [38], f-measure [38], and Matthews correlation coefficient (MCC) [39]. In fact, the approach can achieve correctly classified objects better than other traditional machine learning classifiers. As shown in Table 2, the percentage of correctly classified objects using our approach is 94%. However, the percentages of correctly classified objects for KNN Naive Bayes, MLP, Random Forest, and SVM are 86.1%, 84%, 92.7%, 90.7%, and 93.2%, respectively. Table 3 shows the detailed accuracy of candidate classifiers per class. It consists of six activations: lying, running, standing, walking, stepping upstairs, and stepping downstairs. It reports the activity precision per row, and the last row concludes the overall precision of each algorithm over all activities. Table 3 emphasizes that the precision values of traditional machine learning classifiers are less than our approach in all activities as well as the overall precision shown in Figure 7. Tables 4-6 compare among candidate activity recognition solutions in terms of recall, f-measure, and MCC based on each activity as well as overall recall, f-measure, and MCC values. These tables indicate that SVM outperforms the approach for two activities: walking and walking downstairs. However, the approach has the best recall, f-measure, and MCC for the other 4 activities as well as the best overall average of activities. Moreover, Figures 8-10 depict bar chart comparisons of candidate solutions in terms of recall, f-measure, and MCC, respectively. Finally, Table 7 indicates the overall processing time in seconds required for each classifier in both the learning and testing phases. The processing time of Naive Bayes is the minimum. However, the approach comes in third place after the Naive Bayes and SVM algorithms. Therefore, it has an acceptable processing time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier/Class** & **KNN** & **Random Forest** & **MLP** &
\begin{tabular}{c} **Naive** \\ **Bayes** \\ \end{tabular} & **SVM** & **Approach** \\ \hline LAYING & 1.000 & 1.000 & 0.998 & 0.998 & 1.000 & 1.000 \\ SITTING & 0.684 & 0.833 & 0.890 & 0.574 & 0.837 & 1.000 \\ STANDING & 0.870 & 0.895 & 0.878 & 0.930 & 0.927 & 1.000 \\ WALKING & 0.960 & 0.964 & 0.978 & 0.895 & 0.994 & 0.899 \\ WALKING DOWNSTAIRS & 0.786 & 0.840 & 0.917 & 0.752 & 0.929 & 0.817 \\ WALKING UPSTAIRS & 0.841 & 0.894 & 0.894 & 0.854 & 0.894 & 0.926 \\ Overall Recall & 0.861 & 0.907 & 0.927 & 0.840 & 0.931 & 0.940 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed Recall by Class.  
Figure 8: Detailed Recall by Class.  
Figure 7: Detailed Precision by Class.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier/Class** & **KNN** & **Random Forest** & **MLP** &
\begin{tabular}{c} **Naive** \\ **Bayes** \\ \end{tabular} & **SVM** & **Approach** \\ \hline LAYING & 1.000 & 1.000 & 0.995 & 0.994 & 1.000 & 1.000 \\ SITTING & 0.710 & 0.828 & 0.856 & 0.690 & 0.851 & 0.980 \\ STANDING & 0.763 & 0.845 & 0.868 & 0.767 & 0.871 & 0.976 \\ WALKING & 0.876 & 0.896 & 0.921 & 0.809 & 0.940 & 0.929 \\ WALKING DOWNSTAIRS & 0.833 & 0.890 & 0.942 & 0.766 & 0.942 & 0.878 \\ WALKING UPSTAIRS & 0.815 & 0.871 & 0.891 & 0.825 & 0.904 & 0.929 \\ Overall MCC & 0.835 & 0.890 & 0.912 & 0.812 & 0.918 & 0.948 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detailed MCC by Class.  
Figure 10: Detailed MCC by Class.  
Figure 9: Detailed F-measure by Class.