# Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques
## 6 Cluster Interpretation and Feasible Recommendations
### Solution 1: Variables Selected by the RFE Method and Classified by K-Means Algorithm  
The model was created with the variables _ACCx_, _ACCx_neg_, _ACCy_, _YR_, and SWRS, which were selected using correlation and the recursive feature elimination method detailed in Section 4.2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Clusters** & \multicolumn{3}{c}{**Clustering Algorithm**} \\ \hline  & **Spectral** & **Agglomerative** & **K-Means** \\ \hline \(N=5\) & 0.3269 & 0.3581 & 0.3978 \\ \hline \(N=6\) & 0.2871 & 0.3492 & 0.3791 \\ \hline \(N=7\) & 0.2652 & 0.3159 & 0.3394 \\ \hline \(N=8\) & 0.2736 & 0.29246 & 0.3375 \\ \hline \(N=9\) & 0.2675 & 0.2995 & 0.3149 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Obtained silhouette coefficients for feature-selected variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Clusters** & \multicolumn{3}{c}{**Clustering Algorithm**} \\ \hline  & **Spectral** & **Agglomerative** & **K-Means** \\ \hline \(N=5\) & 0.2727 & 0.3053 & 0.3133 \\ \hline \(N=6\) & 0.2468 & 0.2545 & 0.3171 \\ \hline \(N=7\) & 0.2714 & 0.2623 & 0.3034 \\ \hline \(N=8\) & 0.2763 & 0.2674 & 0.3051 \\ \hline \(N=9\) & 0.2798 & 0.2695 & 0.3048 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Obtained silhouette coefficients for PCA-generated variables.  
The mean value of MSDVxy for each cluster is displayed in Table 6, whereas the mean of the driver-controlled variables for each of the groupings is shown in Figure 7. The generated 5 clusters ordered from C1 (Green) to C5 (Black) are interpretable for the following reasons:  
* C1 (Green): BP, SWA, and SWRS values are low. It also has one of the slowest VSs. It stands out that while ERPM and GP values are high compared to other clusters, motion sickness is the lowest of all. No recommendation would be given to a window of this color.
* C2 (Cyan): It contains the highest average VS of all clusters; the SWA and SWRS variables are very low (similar to those in the C1 cluster), but the BP variable shows moderate use. A more careful and smooth brake pedal usage or slower speeds would reduce the medium motion sickness of this cluster.
* C3 (Purple): While the motion sickness sensation is just a bit higher than that of C2, the causes are completely different; samples in the C3 cluster show a correct and small BP usage combined with low VS and similar motor and acceleration values to C1. The SWA and SWRS variables are the key difference. A reduction in sharp turns would reduce the steering wheel use, the most important factor in the motion sickness of this cluster.
* C4 (Red): A high BP usage with sharp SW actions. Red shows a high level of motion sickness. Reducing the brake pedal and the steering wheel's aggressive use would be the most direct recommendation.
* C5 (Black): It is the smallest cluster, and it is just a more extreme version of the red cluster, with similar BP and higher SWA and SWRS values. The same recommendations as the ones for C4 apply.  
\begin{table}
\begin{tabular}{c c} \hline _Clusters (C\({}_{i}\))_ & _MSDVxy_ \\ \hline _C1 (Green)_ & 0.15 \\ \hline _C2 (Cyan)_ & 0.29 \\ \hline _C3 (Purple)_ & 0.36 \\ \hline _C4 (Red)_ & 0.49 \\ \hline _C5 (Black)_ & 0.75 \\ \hline \end{tabular}
\end{table}
Table 6: The obtained MSDVxy average value when all samples of each cluster (C\({}_{i}\)) of Solution 1 are considered.  
Figure 7: Spider graphs of low (**a**), medium (**b**), and high (**c**) MSDVxy clusters in the models trained with the RFE chosen 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.