## 4 Discussion  
Continuing with the approach and simulation results presented in this paper, a longitudinal study using real patient data is necessary to further investigate the effectiveness and robustness of the methods. While the proposed methodology shows potential, further testing is required to refine and develop it into a practical early-diagnosis device. The proposed methodology has potential applications in other situations, including motor performance in multiple sclerosis [70], stability of balance control [71; 72], characterizing slow eye movements [73], wearable health monitoring [74], and cerebral autoregulation [75; 76]. The sequence of steps required to implement the proposed approach in a clinical setting is described in Figure 8. In summary, a subject performs a series of clinical movement control tests on several occasions over a period of time (not necessarily at equal intervals) and all data are recorded. After a few clinical movement control tests spanning a significant amount of time (say several months or years), using an algorithm (explained in Section 2.3.1), poles are estimated from these data for a particular individual. Finally, a classification algorithm is applied to the estimated poles. The output of the classification algorithm classifies whether a particular individual is healthy or has PPD. This classification will directly aid clinicians in identifying individuals who are at risk of developing PD in the near future and taking necessary precautions or applying certain interventions as appropriate. One possible clinical task with a simple pupilometer outlined in Section 3.2 can be utilized for generating data. Further, one can also choose other clinical tasks, as discussed earlier, with a step input (step change), as this is easy to implement while at the same time yielding a response that can directly be used to estimate the poles of the system. An example of a step input is a step change in the circular target position, that is, considering a circular target initially at some arbitrary position, and the target instantly moves to another position and stays there. The present study pertains to the movement-based diagnosis of Parkinson's disease (PD) and can be compared to previous research in this domain. The authors [14] examined a multitude of studies and found that the mean reported accuracy was approximately 89.1%. Figure 8: Flowchart of a proposed methodology for detecting PPD in a clinical setting. The accuracy of the presented study is superior to this previously reported average and is comparable to the findings of reference [77]. However, the classification accuracy is affected by the amount of feedback noise, as an increase in variance leads to a decrease in accuracy for the same time interval. Nevertheless, the classification accuracy range of the current study remains within the range reported in reference [78]. The authors of reference [79] also conducted a review of various algorithms for the early diagnosis of PD and reported a mean accuracy of 93.84%. In contrast, the accuracy of the current study surpasses this reported average. There are several limitations of the current study. First, we only tested the performance of the trained machine learning model on a relatively small sample size, so future work is needed to validate the performance of the trained model on a large independent data set. Second, the validation of the proposed method with the synthetic data and plausible examples of clinical tasks for a clinical diagnostic process is at a preliminary stage. Hence, future work will focus on a longitudinal study in people who are at a high risk of developing PD and healthy control subjects to validate these findings. With further increase in noise variances, the performance metrics are degraded but can be improved/restored by using a longer time frame for methodology, which suggests that the time frame of the data utilized in the methodology can be used as a design parameter to be tweaked to obtain reliable and robust detection of PPD in real populations.