## 4 Discussion  
In previous studies of stress detection based on the drivedb database, a common method was to take 5 min or less data of different driving sections as a sample, integrate all driving datasets for sample training with the help of a machine-learning method, and detect the stress on two (stress/no stress) or three (low, medium, and high) levels [19; 21; 23; 25]. Through different classification algorithms and combinations of multiple features from different physiological signals, these previous studies achieved good classification results. However, those studies neglected the physiological differences of different individuals in  
Figure 6: GSR signals and marker signals of the six driving sections of Drive02\(\backslash\)04\(\backslash\)06\(\backslash\)08\(\backslash\)11\(\backslash\)13: **(a)** GSR and marker signals of Drive02; **(b)** GSR and marker signals of Drive04; **(c)** GSR and marker signals of Drive06; **(d)** GSR and marker signals of Drive08; **(e)** GSR and marker signals of Drive11; **(f)** GSR and marker signals of Drive13. Drive02 and Drive13 lacked hand GSR signals and only had foot GSR signals. The foot GSR signal of Drive06 was inconsistent with the hand GSR signal, and the study referred to the foot GSR signal. driving stress; they were also incapable of analyzing the impact of abnormal events for stress detection in each driving process. In this study, only one feature of the ECG signals (i.e., _bmNN_) and the simplest k-NN classification algorithm were used to train a network with data from the first half of the driving sections of each driving dataset. This was then used to identify and classify the stress levels of the second half of the driving sections of these driving datasets. This was unprecedented, given that most previous studies merged different driving datasets to classify stress levels on different driving sections. In addition, for different driving datasets, we chose different _bm_ values for stress detection to conduct personalized analysis. Combined with the classification accuracy and waveform changes in the GSR during the rest period, the datasets with abnormal mood fluctuations were determined. The classification results of the abnormal datasets were not counted. Thus, we eliminated the impact of abnormal datasets in the driving stress database to improve the overall accuracy of the stress assessment. Table 5 shows comparisons of this and related works for stress detection using the drivedb database. Based on the optimal _bm_ value selected for each driving dataset, samples of every 200 NNIs (approximately 2-3 min) were classified based on stress. Using the 0\(\backslash\)1 + 2 classification method, the average identification accuracy for the six driving datasets was 93.7%, whereas the average identification accuracy values for the remaining four driving datasets were low. However, the GSR waveforms of these four datasets exhibited abnormal fluctuations during the rest periods, which might explain the low identification accuracy. These 10 driving datasets revealed that the _bmNN_ value had a high correlation with psychological stress, which was reflected in real time by changes in GSR signals.