## 4 Results and Discussion
[8], and our proposed set of a sEMG attached to the forearm combined with an accelerometer attached to the [PERSON] wrist. Table 3 shows that the processed EMG data extracted from the forearm muscle was capable to differentiate between healthy and PD patients. This is clear as the KNN achieved a classification accuracy of 98.6% which is higher than the 89.8% of the processed EMG data taken from biceps muscle. Moreover, when constructing the ensemble classifiers using the EMG data extracted from the forearm muscle using bagged decision tree classifier, the classifications accuracy has slightly increased to 98.8%, which is greater than the 93.1% of the processed EMG data taken from biceps brachii muscle. Figures 15 and 16 show the confusion matrices of the bagged tree classifier in case of EMG measured from the forearm and biceps brachii muscles, respectively. Figures 17 and 18 show the ROC curve for the bagged tree classifier of the EMG signals of the forearm and bicep brachii muscles, respectively. The corresponding areas under ROC (AUC) curves are 1 and 0.98, respectively. Figure 4:1 Two-dimensional scatter plot of ZC feature vs. WL feature of forearm sEMG sensor for normal and PD patients. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Biceps Brachii** & **Forearm** \\ \hline Decision Tree & 87.7\% & 98.1\% \\ LDA & 60\% & 86\% \\ KNN & 89.8\% & 98.6\% \\ SVM & 78.2 & 96.9\% \\ Boosted Tree & 87\% & 98.4\% \\ Bagged Tree & **93.1\%** & **98.8\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracies for EMG sensor. Figure 16: Confusion matrix of EMG measured from biceps brachii muscle bagged tree classifier. Figure 15: Confusion matrix of EMG measured from forearm muscle bagged tree classifier. Figure 17: Receiver operating characteristic (ROC) of EMG measured from biceps brachii muscle bagged tree classifier. Figure 18: ROC of EMG measured from biceps brachii muscle bagged tree classifier. The best achieved accuracy by [PERSON] et al. [9] was 92.98% using a SVM classifier. In our work, using Shimmer sEMG sensor attached to the forearm on one point gave an accuracy of 96.9% for the same classifier. The reason for the difference in classification accuracies, from our point of view, is due to the difference in sampling frequencies between the two sensors. [PERSON] et al. [27] investigated the effect of sampling rate of sEMG sensor data on the classification accuracy, and concluded that the frequency content of EMG signals \(>100\) Hz is critical to the performance of myoelectric systems. They also concluded that the loss of high frequency content degrades the classification accuracies. Myoarmband's original sampling rate is 200 Hz, as stated in the same reference, which is considered a low sampling frequency, and causes the frequency content above 100 Hz to be lost. This in turn affects the classification accuracy. Furthermore, combining readings of the EMG and accelerometer signals taken from the forearm muscle has improved classification accuracy as shown in Table 4. After sensor fusion, the accuracy has reached 99.2% for decision tree classifer, which is higher than the 97.3% when using EMG alone for the bicep brachii muscle. Similarly, the classification accuracy has further increased to 99.6% using bagged and boosted decision tree classifiers constructed using EMG and accelerometer signals taken from the biceps brachii muscle, which is higher than 98.5% of the bagged tree built with the EMG and accelerometer signals taken from the forearm muscle as shown in Table 4. Figures 19 and 20 show the confusion matrices of the bagged tree classifiers in case of sensor data fusion of accelerometer and EMG of the forearm and biceps brachii muscles, respectively. Figures 21 and 22 show the ROC curves of the bagged tree classifier for the sensor fusion of ACC from Palmer wrist and EMG signals of the forearm and biceps brachii muscles, respectively.