## 3 Model and Algorithm Design
### Model Optimization Strategies
#### 3.2.1 Data Augmentation and Regularization  
Data augmentation is a powerful technique for enhancing the generalization ability of machine learning models, especially when data are limited or subject to variability, such as in physiological signal monitoring. In this context, the goal of data augmentation is to artificially increase the diversity of the training dataset by introducing variations that simulate real-world scenarios, thus enabling the model to learn more robust features. The following data-augmentation strategies were employed in this study to improve the performance of the CNN-LSTM combined model:  
Adding Noise: Introducing different types of noise (e.g., Gaussian noise, white noise) to the original physiological signals helps the model become more robust to environmental and sensor-induced interference. By simulating the presence of noise in the data, the model learns to differentiate between valid signal patterns and random disturbances, improving its ability to handle noisy real-world inputs. Temporal Domain Transformation: This strategy involves randomly altering the speed and amplitude of the physiological signals to simulate different physiological states or environmental conditions. For example, adjusting the heart rate or respiratory signal to mimic faster or slower breathing rates can help the model learn the dynamic changes in physiological states more effectively. This transformation helps simulate real-life variability in physiological signals, such as stress, fatigue, or excitement. Signal Clipping and Stitching: In this approach, continuous signal sequences are randomly clipped into shorter segments, and then, these clipped parts are stitched together to create new training samples. This technique generates additional variations in the training set, helping the model to generalize better when faced with diverse driving scenarios. By creating new combinations of signal sequences, the model learns to recognize and handle diverse temporal patterns and transitions in physiological data. By applying these data-augmentation techniques, the model can better generalize to unseen data, ensuring that it remains effective under a wide range of conditions, including varying postures, environmental factors, and physiological states. These strategies play a crucial role in improving the model's robustness and performance, especially in complex, noisy, and dynamic driving environments. Through these data-augmentation techniques, the model can better adapt to the complex variations in physiological signals encountered during actual driving, thus improving performance during the testing phase. To further enhance the model's generalization ability and prevent overfitting, regularization methods are introduced during model training [15]. A weight decay term is incorporated into the loss function to prevent the model parameters from overfitting the training data. The L2 regularization formula is expressed as follows:  
\[L_{total}=L_{task}+\lambda\sum_{i}W_{i}^{2} \tag{18}\]  
where \(L_{total}\) is the total loss, \(L_{task}\) is the task loss, \(\lambda\) is the regularization coefficient, and \(W_{i}\) represents the model weight parameters. During the training process of the neural network, a portion of neurons is randomly dropped to reduce model complexity. Dropout can prevent excessive dependence between neurons, thereby enhancing the model's performance based on testing data [13]. The application od Dropout can be expressed with the following formula:  
\[y_{i}=\frac{f(x_{i})}{1-p},p\in[0,1] \tag{19}\]  
where \(f(x_{i})\) is the activation function of the neuron, and \(p\) is the dropout probability. To optimize the model's predictive performance, the target function designed in this study is oriented towards accuracy and robustness, considering both classification loss and regression loss comprehensively.