## 5 Discussion  
In this work, we exploited pressure sensor insole data, derived from three groups of participants (Adults (S), Elderly (EL), and PD patients) performing the Smart-Insole Gait Assessment Protocol for PD patients. We have extracted several spatial and temporal features and employed feature selection methods to identify the most significant. A variety of estimators were tested in attempts to separate PD patients from healthy controls and to distinguish between PD patients' ON/OFF medication states. Finally, we investigated the possibility of discriminating between the severity levels of PD, derived from MDS-UPDRS: Part III Total Score. Machine learning models were trained using gait features, while three classification algorithms were tested, seeking to identify those with the highest success rates. Our statistical analysis shows that various gait extracted features can be used to differentiate the medication state of PD patients as well as the severity levels of the disease (Table 7). To differentiate the medication status (ON/OFF) proved to be the most challenging. Based on our findings, the ON/OFF medication state of PD patients is associated with a variety of different gait features for each test. Notably, the Swing Time is significantly affected by the medication state in all tests. Moreover, the Step Number and the Single Support Time significantly correlate in the WST- Slow and High tests, while Step Frequency and Walk Ratio significantly correlate in the WST- Slow and Normal tests. Apart from this comparison, a number of gait features showed the potential to be useful indicators of PD severity level, based on MDS-UPDRS Part III. Generally, it appears that Swing Time (s), Single Support (%), Stance Phase (%), and Swing Phase (%) are the gait-related features that are highly affected by the severity levels of the disease. From the results presented in Table 8, the highest performance was observed during the binary classification between PD and non-PD participants, achieving 88% by employing the RF classifier during the WST-Slow test. However, splitting the \"non-PD\" class into healthy elderly and adult subjects proved difficult, resulting in a considerable reduction of the model's performance, achieving a 77% accuracy with RF classifier during WST-Slow test. In terms of predicting the medication state of PD patients, the highest performances were observed during the mTUG and WST-High tests, with up to 73% correct predictions when using the ET estimator. This finding confirms the results of the statistical analysis, as these two tests require more effort from the participant and therefore cause greater physical fatigue. Consequently, by observing the results in the test requiring a higher physical stress, it is evident that the administration of dopamine drug enhances muscular movements and durability during walking. Regarding the identification of severity levels based on the MDS-UPDRS: Part III ratings, a prediction rate of 81% was observed in the mTUG test and 77% in the WST-Slow test, using the AB algorithm. The analysis of both ON and OFF medication states is crucial to fully describe PD patient's performance but also to study the effect of levodopa which relates patients' responsiveness to a large number of factors including patients PD type and PD severity level [62; 63]. Moreover, contradictory findings may be found in the literature regarding features association with gait velocity, possibly due to the fact that the notion of \"normal gait velocity\" is strongly subjective and a variety of features are pace-related [31; 54; 55]. Our findings indicate the presence of different dominant features in different walking speeds (see Table 7), which is in line with literature findings indicating that the effect of the ON phase in the spatial and temporal features is mediated by self-selected gait velocity [64].