## 5 Conclusions/Outlook  
In this study, the features of parkinsonian tremor and its quantitative assessment methods have been presented. A time-frequency signal analysis algorithm for tremor state detection was adopted. After the ten-second tremor-assessment task, the task state is judged, form both frequency and time domains. The task with an invalid state will be discarded, while the signal with a valid state will be further processed via a least-square-estimation model. The peak powers of all axes of accelerations and angular velocities are used to the linear regression analysis. The regression coefficients are different for different tremor types. A better cognition of the analytical methods in the system based on an IMU was achieved with the validation results. In comparative experiments with an EM tracking system, we showed that the raw data of an IMU correlated well with the severity of imitated tremor tasks (_r_ = 0.996). Results also show that inconsistent tremor actions resulted in smaller tremor amplitude when using the PSD method. On the other hand, tremor amplitude increased due to that fact that some sampling points were missing. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Subject** & **ln(_acc. power_)** & **ln(_gyro.power_)** & **ln(_acc. RMS_)** & **ln(_gyro. RMS_)** & **R** & **Clinical Score(_D_)** \\ \hline Patient 1 & \(-8.52\) & \(0\) (\(-6.27\)) & \(-2.95\) & \(0.30\) & \(0\) (\(-5.41\)) & \(0.0\) \\ Patient 2 & \(-7.42\) & \(0.78\) & \(-2.42\) & \(1.93\) & \(0\) (\(-4.01\)) & \(0.5\) \\ Patient 3 & \(-3.06\) & \(7.05\) & \(-0.92\) & \(3.92\) & \(1.27\) & \(1.0\) \\ Patient 4 & \(-4.10\) & \(7.37\) & \(-1.40\) & \(4.14\) & \(1.36\) & \(1.5\) \\ Patient 5 & \(-2.74\) & \(7.87\) & \(-0.58\) & \(4.31\) & \(1.99\) & \(2.0\) \\ Patient 6 & \(-2.12\) & \(7.55\) & \(-0.49\) & \(4.22\) & \(2.06\) & \(2.0\) \\ Patient 7 & \(-1.95\) & \(11.21\) & \(-0.22\) & \(5.61\) & \(2.81\) & \(3.0\) \\ \(r\) & \(0.88\) & \(0.93\) & \(0.91\) & \(0.93\) & \(0.98\) & / \\ \(a\) (2-tailed) & \(0.008\) & \(<0.01\) & \(0.004\) & \(0.002\) & \(<\)0.01 & / \\ \hline \end{tabular}
\end{table} 
Table 1: Results for rest and postural tremor assessmentsNext, the clinical experiments were performed. The clinical experiment showed the assessed severity correlated well with the judgments of a neurologist (\(r=0.98\)). In addition, the dominant tremor frequency was constant or had only small fluctuations even if the tremor amplitude changed during the ten-second task period. This study provides the necessary science and engineering to guide future signal processing methods. Then future works should involve following points:  
1. The relation between the peak power of IMU signals and the consistency of IMU signals in PD patients should be studied [43, 44]. 2. The repeatability of tremor amplitude with the same patient at different times should be investigated further [45]. 3. The regression coefficients in Equation (5) are different according to tasks and tremor types. More clinical experiments are needed to modify the scale factors and coefficients in the tremor amplitude calculation. In addition, essential tremor and other types of pathological tremors need to be quantitatively assessed. 4. Together with the assessments of bradykinesia, rigidity, and the side effects of medication or surgical treatments such as dyskinesia, paresthesia, and other non-motor symptoms, more parameters should be evaluated in a single system for the overall assessment of parkinsonian symptoms [16, 46]. In addition, there are two main trends in the development of MEMS motion sensors: more sensors embedded in a single chip and more accurate sensor fusion algorithms. These two points make it easier for the future system to detect the hand tremor with even smaller dimensions and greater accuracy. For example, a three-axis compass can remove the yaw drift of the IMU fusion output. Thus, more accurate orientation values, even location values, can be obtained.