# Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques
## 6 Cluster Interpretation and Feasible Recommendations
### Solution 2: Variables Obtained by the PCA Method and Classified by K-Means Algorithm  
The model was created with the five PCA variables defined in Section 4.2. The mean value of MSDVxy for each cluster is displayed in Table 7, whereas the means of the driver-controlled variables for each grouping is shown in Figure 8. The different meanings and recommendations for the clusters are as follows:  
* D1 (Green): Baseline cluster that shows very good driving with the lowest motion sickness. GP and PGP have medium values but do not translate into motion sickness. It is also characterized by very low values in everything else, BP pressure, SW, and VS. No recommendation is needed here.
* D2 (Cyan): With the second-lowest motion sickness value, this cluster group samples with very high ERPM and medium GP, PGP, and VS, but shares low BP and SW values in D1. No recommendation is needed here.
* D3 (Blue): The same motion sickness as D3, but instead of abrupt BP usage, D4 groups the samples with even higher GP values, combined with medium VS. Smoother accelerations would result in direct improvements.
* D4 (Purple): Medium motion sickness; holds the highest average VS with medium-high BP values. Other variables are either medium or low. Driving slower or smoother brake pedal usage would be ideal recommendations.  
These first four clusters do not have high steering wheel usage, so recommendations need to be focused on speed and pedals.  
* D5 (Orange): The first cluster with medium SWA and SWRS values; it does not have any high or distinguishable high-variable value. It has low BP and VS. A smoother steering wheel usage would significantly improve the generated motion sickness.  
\begin{table}
\begin{tabular}{c c} \hline \hline _Clusters (D\({}_{i}\))_ & _MSDVxy_ \\ \hline _D1 (Green)_ & 0.11 \\ \hline _D2 (Cyan)_ & 0.19 \\ \hline _D3 (Blue)_ & 0.26 \\ \hline _D4 (Purple)_ & 0.26 \\ \hline _D5 (Orange)_ & 0.31 \\ \hline _D6 (Red)_ & 0.49 \\ \hline _D7 (Black)_ & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The obtained MSDVxy average values when all samples of each cluster (\(D_{i}\)) of Solution 2 are considered.  
Figure 8: Spider graphs of low (**a**), medium (**b**), and high (**c**) MSDVxy clusters in the models trained with PCA-transformed variables. The visualized variables can be represented with values in the [0, 1] range since they are normalized. The combination of the mean of each variable gives us the characteristic form of each cluster.  
* D6 (Red): High motion sickness cluster related to very high SWA and SWRS values with aggressive BP management. The improvement of the problematic variables (steering wheel and brake pedal) would make the samples closer to the D5 cluster.
* D7 (Black): High motion sickness cluster related to high SWA and SWRS values with aggressive BP management and high VS. Comfort improvement could be achieved by smoother steering wheel usage, slower driving, and less aggressive braking. The main difference with D6 is the higher vehicle speed and ERPM.  
While the cluster numbers are higher than in the previous Section 6.1, many of the generated structures from the previous solution also appear here. The worst clusters, red (C4 and D6) and black (C6 and D7), are very similar in both models sharing high BP, SWA, SRWS, and medium speeds. The C3 cluster of Solution 1 is very similar to the D5 of Solution 2 with medium-high SWA, SWRS, ERPM, GP, and PGP. The C2 cluster of Solution 1 is also similar to the D4 cluster of Solution 2, obtaining the characteristically high BP, VS, and little of everything else. The most comfortable cluster of Solution 1, C1 (Figure 7), also seems to be a mixture of the comfortable D1 and D2 clusters of Figure 8 with D1 obtaining the lower end of ERPM and VS, and D2 obtaining the higher end of these variables.  
This last model shown in Figure 8 is used in the following sections, where driving style and temporal evolution are studied; thus, Table 8 summarizes the clustering driving patterns and driving advice recommendations in each case.