# A Study of a Ride Comfort Control System for Ultra-Compact Vehicles Using Biometric Information
## 8 Results and Discussion  
### Prediction of Ride Quality by Composite Frequencies  
In Section 4, the comfort level was calculated using a multiple regression analysis of the biometric information of 25 experimental collaborators. However, the significance of the \(p\)-value could not be confirmed under each condition. In Section 5, the group was divided by experimental collaborators who showed similar characteristics of biometric information when their eyes were closed at rest. In Section 6, after grouping, we focused on a group with many people, performed principal component analysis, and extracted multiple evaluation factors for the biometric information. We performed a multiple regression analysis based on the calculated principal components, but when we tried to predict the psychological state based on the multiple regression equation, we could not control it in real time because we did not know which biometric information would be the coefficient. In Section 7, to solve the problems in Section 6, multiple regression analysis was performed using the biometric information of the first principal component, which has a high contribution rate.  
Figure 13: Results of multiple regression analysis. (**a**) 0.2-3 Hz; (**b**) 4–8 Hz; (**c**) 8–20 Hz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Vibration Condition**} \\ \cline{3-5} \(i\) & **Explanatory Variable** & **0.2–3 Hz** & **4–8 Hz** & **8–20 Hz** \\ \cline{3-5}  & & & \(b_{i}\) & \\ \hline
0 & Constant & 12.0 & 0.367 & 13.3 \\ \hline
1 & LF/HF (0.2–3 Hz) & \(-0.123\) & \(-0.211\) & 0.640 \\ \hline
2 &
\begin{tabular}{c} LF/HF normalization on eyes closed \\ state at resting time (0.2–3 Hz) \\ \end{tabular} & \(1.949\) & \(-1.735\) & \(-2.341\) \\ \hline
3 &
\begin{tabular}{c} LF/HF (4–8 Hz) \\ state at resting time (4–8 Hz) \\ \end{tabular} & \(1.775\) & \(-1.260\) & \(3.869\) \\ \hline
4 &
\begin{tabular}{c} LF/HF normalization on eyes closed \\ state at resting time (4–8 Hz) \\ \end{tabular} & \(-0.388\) & \(-3.845\) & \(-5.852\) \\ \hline
5 &
\begin{tabular}{c} LF/HF (8–20 Hz) \\ \end{tabular} & \(-11.342\) & \(5.260\) & \(-12.701\) \\ \hline
6 &
\begin{tabular}{c} LF/HF (Rest time) \\ \end{tabular} & \(-1.620\) & \(0.871\) & \(1.145\) \\ \hline
7 & Right cerebral blood flow (4–8 Hz) & \(0.945\) & \(-0.201\) & \(-2.249\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Explanatory variables and their values.  
A high correlation was confirmed for each condition. However, in the high-frequency band of 8-20 Hz, a value higher than the \(p\)-value, which is considered significant, was confirmed. Under the vibration conditions of 0.2-3 Hz and 4-8 Hz, the correlation is high, and it can be evaluated as a superior formula from the \(p\)-value; and, it is considered that the psychological state can be accurately estimated in the corresponding frequency band. At 8-20 Hz, the correlation was high, but the \(p\)-value was not a dominant equation. The reason the \(p\)-value was high and the equation was not dominant at 8-20 Hz is that the experimental collaborators concentrated on the answers they evaluated as unpleasant at that frequency. Therefore, it is conceivable to control high-frequency components, such as 8-20 Hz, from the beginning so that they are not input to the occupants.