###### Abstract
[15] built a model to classify the drowsy state; they only performed a comparative analysis of the signals in those two states. Hence, the effectiveness of EDA and RESP in detecting driving drowsiness remains unknown. Photoplethysmography (PPG) is one of the most common clinical signals used to measure pulse [22]. [PERSON], et al. [18] collected PPG signals through a wearable device to detect driving drowsiness. The accuracy was 70%, which easily caused misjudgment. Thus, it was not satisfactory in actual driving scenarios. Table 1 shows the scientific gap in existing driving drowsiness detection features. Therefore, researchers have attempted to combine these physiological signals to detect drowsiness and guarantee higher accuracy. For example, [PERSON] [23] collected EDA, RESP, and PPG signals from eight rail drivers. He adopted the k-nearest neighbors method \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Features** & **Defects** \\ \hline \multirow{3}{*}{Driving behavior} & Vehicle data (speed, acceleration, \\  & steering wheel angle, \\  & lane center offset, etc.) & Low accuracy (75\%) \\ \hline \multirow{2}{*}{Facial images} & Facial features & Cannot wear glasses or facial masks \\  & Eye movements & Does not match natural driving scenes \\ \hline \multirow{4}{*}{Physiological signals} & Electroencephalogram (EEG) & Too intrusive to collect during the actual \\  & Electrocardiogram (ECG) & driving process \\ \cline{1-1} \cline{2-3}  & Electrodermal activity (EDA) & Accuracy for driving drowsiness detection \\ \cline{1-1}  & Respiration (RESP) & remains unknown \\ \cline{1-1} \cline{2-3}  & Photoplethysmography(PPG) & Low accuracy when only using single signals to \\ \cline{1-1} \cline{2-3}  & identify driving fatigue. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The scientific gap in existing driving drowsiness detection features. and support vector machines to detect the drivers' drowsiness. In his study, the highest accuracy reached 85%. As explained above, the combination of EDA, RESP, and PPG is sufficient to improve accuracy but cannot meet the needs of application in real driving scenarios. This may be due to redundant information in the data or defects in the classification algorithms. Too many variables in the data may contain redundant information, which would increase the complexity of computation. Principal component analysis assists in removing the redundant information that may exist in the data. The present study adopted principal component analysis (PCA), one of the most commonly used dimensionality reduction methods [24]. Detecting driver drowsiness requires adequate models. Some classification methods, such as support vector machine and k-nearest neighbors, have defects that reduce the model's accuracy or take a long time to classify high-dimensional data, leading to low accuracy and a high time delay. Artificial neural networks can quickly and thoroughly approximate arbitrarily complex linear or nonlinear relationships and have strong robustness and fault tolerance [25]. Considering this, we adopted artificial neural networks to classify the noise-removal data. This study proposed a hybrid model utilizing principal component analysis and artificial neural networks to detect driving drowsiness quickly and accurately. Physiological data (EDA, RESP, and PPG) were non-intrusively collected by wearable devices and used as the model's input. Then, we designed three comparison models: a single model with artificial neural networks, a hybrid model utilizing principal component analysis and classic machine learning algorithms, and a single model with classic machine learning algorithms. This research analyzed the impact of principal component analysis on driving drowsiness detection and compared the performance of artificial neural networks and classic machine learning algorithms. Two hypotheses were proposed:  
**Hypothesis 1**. : _Removing noise that may exist in the data can improve accuracy and shorten training time._  
**Hypothesis 2**.