## 4 Results and Discussion  
The results as shown in Figures 3 and 4 show that the accelerometer data of a PD patient has higher amplitude as compared to a normal test person. This is due to the effect of tremor of PD patients. Figure 3: Right side acceleration of the healthy test person. Figures 5-8 show that the pattern of bursts in processed EMG data calculated from forearm muscle, which is responsible for hand movement, of PD patient is more recognizable than healthy controls. Also, the EMG signal of PD patient has spikes, and is more affected by the pattern of bursts as compared to the healthy test person data. In addition, the EMG signal measured from the biceps brachii muscle shows some coherence with the acceleration signal in the case of PD patient. Figure 4: Right side acceleration of the Parkinson’s disease (PD) patient. Figure 5: EMG (forearm) signal of a healthy person. Figures 9 and 10 show two-dimensional scatter plots based on the feature vectors of wrist accelerometer data for normal and PD patients. Figure 9 represents the standard deviation versus the mean of accelerometer data for normal and PD patient classes. Figure 10 represents the maximum value features versus minimum value features of accelerometer data also for normal and PD patient classes. As, can be seen, both figures show good separation between the two classes. Figures 11 and 12 show two-dimensional scatter plots based on the feature vectors of the biceps brachii muscle, and Figures 13 and 14 show the corresponding scatter-plots for the forearm muscle. Both Figures 11 and 13 represent the two-dimensional scatter-plots of the number of SSC versus MAV of the processed sEMG data of the biceps brachii and forearm muscles, respectively. As, can be visually inspected, the forearm muscle has a better class seperability between normal and PD patients compared to the biceps brachii muscle. On the other hand, Figures 12 and 14 represent the two-dimensional scatter-plots of the ZC versus WL of preprocessed sEMG data of the biceps brachii and forearm muscles, respectively. Also, the forearm muscle shows a better discrimination between the normal and PD patient classes. Figure 6: EMG (forearm) signal of the PD patient. Figure 7: EMG (Biceps) signal of a healthy person. Figure 8: EMG (Biceps) signal of a PD patient. Figure 9: Two-dimensional scatter plot of the standard deviation feature vs. the mean feature of wrist accelerometer sensor for normal and PD patients. Figure 11: Two-dimensional scatter plot of slope sign changes (SSC) feature vs. mean absolute value (MAV) feature of biceps brachii surface EMG (sEMG) sensor for normal and PD patients. Figure 10: Two-dimensional scatter plot of the maximum value feature vs. the minimum value feature of wrist accelerometer sensor for normal and PD patients. Figure 12: Two-dimensional scatter plot of zero crossing (ZC) feature vs. waveform length (WL) feature of biceps brachii sEMG sensor for normal and PD patients. Figure 4:0 Two-dimensional scatter plot of SSC feature vs. MAV feature of forearm sEMG sensor for normal and PD patients. To test the performance of the proposed approach and compare the results to the relevant approaches reported in the literature, several machine leaning classifiers were constructed. As it is hard to compare absolute achieved accuracies for different datasets and scenarios, in our work we investigated two measurement sets. The first set included a sEMG attached to the biceps brachii muscle and an accelerometer attached to the [PERSON] wrist as in [8], and the second set, our proposed measurement set, included a sEMG attached to the forearm and an accelerometer attached to the [PERSON] wrist. Furthermore, in our results we compared the classification accuracies of four different scenarios namely, sEMG attached to the biceps brachii muscle, sEMG attached to the forearm as proposed by [PERSON] et al. [9], sEMG attached to the biceps brachii muscle combined with an accelerometer attached to the [PERSON] wrist as proposed by [PERSON] et al.