# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 4 Dataset Preparation
### Data Pre-Processing  
This subsection focuses on the pre-processing of the features of the collected data. First, the descriptions of the personnel sign data and the internal environment data are shown in Table 5.  
In this paper, three sets of real-world engineering data were used for training, which resulted in a tunnel personnel safety detection alarm model. The statistical features of the data are shown in Table 6. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Marking**} & \multirow{2}{*}{**Name of**} & \multirow{2}{*}{**Length of**} & \multirow{2}{*}{**Tunnel**} & \multirow{2}{*}{**Upper**} & \multirow{2}{*}{**of Main**} & \multirow{2}{*}{**Distance from**} & \multirow{2}{*}{**Relative Position**} \\
**Section** & & & & & & \\
**Branch Hole** & & & & & & \\
**Branch Hole (m)** & & & & & & \\ \hline Second bid: 2 + & 1\# branch & \multirow{2}{*}{325} & \multirow{2}{*}{3420} & Upper: 1785 & 49 & 374 & 8 \\
095-5 + 515 & & hole (3 + 880) & & Lower: 1635 & 61 & 386 & 8 \\ Third bid: 5 + & 2\# branch & \multirow{2}{*}{494} & \multirow{2}{*}{2690} & Upper: 1815 & 167 & 661 & 14 \\
515-8 + 205 & & hole (7 + 330) & & Lower: 875 & 100 & 594 & 12 \\ Fourth bid: 8 + & 2\# branch & \multirow{2}{*}{380} & \multirow{2}{*}{2085} & Upper: 1175 & 137 & 517 & 11 \\
205–10 + 290 & & hole (9 + 400) & & Lower: 890 & 165 & 545 & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic information of tunnel engineering. Figure 6: Schematic diagram of information collection on construction personnel. \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Data Name**} & \multicolumn{1}{c}{**Unit**} & \multicolumn{1}{c}{**Remarks**} \\ \hline Ambient Temperature & Celsius (\({}^{\circ}\)C) & Temperature inside the cave in the area of the operator \\ Ambient Humidity & \% & Humidity in the cave in the area where the operator is located \\ Relative Location & 1 & Relative position correspondence rule (see Figure 5, Table 3) \\ Body Temperature & Celsius (\({}^{\circ}\)C) & Skin surface body temperature sensor collection \\ Heart Rate & BPM & Heart rate sensor collection \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Descriptions of personnel safety testing data. On the basis of the collected data, the actual personnel security status is judged and manual alarm tagging is performed, specifically tagging for two classes, i.e., normal status and alarm status. Due to the small amount of tagging data and consequent need for a data enhancement process, the specific method is based on the original data for random tagging and fine-tuning, that is, the original data is modified with random dithering and class tagging. This ensures the randomness of the data, and the linear fine-tuning makes the data more realistic, thus increasing the generalization of the model. To increase the generalizability of our classifier, we may first randomize the perturbation points along the distribution by adding some values from a random distribution. A model trained on these data is more generalizable to those sample data points that are not included in the training set. The smoothed curve is shown in Figure 7a, and the part after performing the data enhancement process appears as discrete points as shown in Figure 7b. The details of the experiments are shown in the Experiments section. 1. For body temperature data, 7% to 10% of each dataset is selected for outlier labeling, and the treatment is to add incremental values conforming to a normal distribution (\(-1\) to \(2\)) to the actual data.