## 3 Results
### Interpretation of Results  
Based on the \(0\backslash 1+2\) classification method, when the six driving datasets (Drive02\(\backslash\)04\(\backslash\)06\(\backslash\)08\(\backslash\)11\(\backslash\)13) used the optimal \(bmNN\) as the feature value, the average identification accuracy reached 93.7%, which was higher than the 76.1% identification accuracy achieved when \(avNN\) was used as the feature value. This indicates that the \(bmNN\) value was more closely related to driving stress than was the average heart rate, and selecting the \(bm\) value achieved the classification of driving stress. However, the classification results of Drive11\(\backslash\)13 when \(avNN\) was used as the feature value were 100% accurate, which was higher than that achieved when using \(bmNN\) as the feature value. The superior performance of \(avNN\) as a feature value for Drive11\(\backslash\)13 identification might be related to specific individuals. Further experiments are needed to discover whether the stress level changes according to \(avNN\) or \(bmNN\) and if there are certain rules regarding this change. When \(bm\) equaled 12, the identification accuracy of Drive13 was only 55.2%; however, when \(bm\) equaled 15, the identification accuracy increased to 90.6%. Owing to this significant difference in identification accuracy, it was speculated that \(bm\), which represents stress level, could vary across different driving experiments. For Drive13, the stress expression was closely related to \"1111\". Table 3 shows the average \(bmNN\) values during different driving sections of the six driving datasets (Drive02\(\backslash\)04\(\backslash\)06\(\backslash\)08\(\backslash\)11\(\backslash\)13). It was necessary to focus on the changes in Drive04, because for the other five driving datasets, the average feature values of \(bmNN\) for the different driving sections followed the same order of Rest > Hwy > City. This was different for Drive04; when \(bm\) was 12, the average values of \(bmNN\) were Rest (0.117) > City (0.063) > Hwy (0.028); when \(bm\) was 4, the average values of \(bmNN\) were Hwy (0.127) > City (0.074) > Rest (0.031). For Drive04, although the different driving sections all showed significant changes in the average \(bmNN\) value, the trends were different when \(bm\) took the reference value of 12 compared to when \(bm\) took the optimal value of 4. The unexpected events during the driving process and the personality of the driver were the reasons that led to these changes. Table 4 shows the identification results for the remaining four datasets (Drive07\(\backslash\)10\(\backslash\)12\(\backslash\)15). With the optimal \(bm\) value, the corresponding \(bmNN\) feature identification accuracy was respectively 73.2, 57.9, 56.5, and 63.4%, whereas the identification accuracy for category 0 alone was respectively 48.6, 17.9, 15.6, and 30.8%. For category \(1+2\) alone, it was 98.6, 96.6, 91.9, and 100%. The k-NN classification algorithm resulted in almost perfect identification accuracy for category \(1+2\); however, its performance decreased for category 0. This might be because, during the rest periods for these four driving datasets, unexpected events occurred that led to intense emotional fluctuations. Subsequently, the GSR signals of all datasets during the rest period were compared to verify the above speculation. Figure 5 shows the GSR waveforms of the four driving datasets with an accuracy below 80%, whereas Figure 6 shows the GSR waveforms of the six driving datasets with an accuracy above 80%. Figure 5a,b show that during Rest2 of Drive07\(\backslash\)10, the GSR waveforms showed abnormal fluctuations. During Rest1 and Rest2 of Drive12, the GSR waveforms also showed abnormal fluctuations, which manifested in the foot GSR signals in Figure 5c. During Rest1 of Drive15, the GSR waveforms showed abnormal fluctuations, as well (see Figure 5d). As a result, the GSR waveforms during the rest periods of these four driving datasets showed