# Physiological Evaluation of User Experience in Unstable Automated Driving: A Comparative Study
## 2 Research Methodology
### Analysis Method  
To analyze the user's evaluations of the A.D(S) and A.D(C) scenarios, we comprehensively used the participants' qualitative rating results and measured physiological changes. For the qualitative rating results, we directly used the 5-point scale ratings from the participants' responses. The section-specific qualitative ratings for frustration and startle were linked to measured physiological changes to increase the credibility of the qualitative ratings. Among the measured physiological changes, the EMG data required normalization due to large differences in measurements between participants, and a number of preprocessing steps were performed to achieve this. Existing studies dealing with EMG data have also performed normalization to correct for measurement differences between participants [36; 37; 38; 65; 66]. Two main normalization methods were used. Maximum Voluntary Contraction (MVC) is a method that measures the subject's maximum EMG value and normalizes the EMG value based on that value [65]. This is mainly used in studies where the measurement is based on the actual activity of the subject. Another method is the min-max normalization method, which uses the minimum and maximum values of the recorded EMG values for each individual subject [66]. This is used to analyze EMG data recorded in an experimental setting, where it is difficult to separately measure the maximum active value of the EMG data, or where the subject is not required to move much. In this study, the min-max normalization method was used because the EMG data were analyzed in a situation where the participants' movements were not required. In this study, the following preprocessing steps were taken to use the measured EMG data for analysis. First, outliers recorded at abnormally high levels were removed. The outliers were removed using the method of adding or subtracting 3 standard deviations from the mean [36; 37; 67]. Data outside the range of 3 standard deviations from the mean were defined as outliers and removed. After outlier removal, the Kalman filter method was applied to smooth the time-series EMG data to maintain the continuity of the removed outlier values and to compensate for measurement delays [68]. Then, the 1500 EMG values recorded per second were grouped into 150 groups in sequence, and the root mean square value for each group was calculated to derive the EMG values at 0.1 s intervals. Based on the preprocessed data, minimum-maximum normalization was performed to organize the EMG measurement results of each participant for analysis. \[\chi^{\prime}=\frac{\chi-\min(\chi)}{\max(\chi)-\min(\chi)} \tag{1}\]  
where \(\chi^{\prime}\) is the normalized value, \(\chi\) is the original value, \(\min(\chi)\) is the minimum value in the dataset, and \(\max(\chi)\) is the maximum value in the dataset. For pupil diameter, measurement results obtained by the device's dedicated analysis program, which records pupil diameter at 1/1000 s intervals, were exported as Excel data and used for analysis. As shown in Table 4, average values of EMG (arm and leg) and pupil diameter were calculated for specific moments to analyze in detail the physiological changes exhibited by the participants for each target section. For the roundabouts, the analysis was divided into 6 moments for the A.D(S) scenario and 4 moments for the A.D(C) scenario. Using the moment before entering the roundabout (Seg. R-1) as the baseline, paired _t_-tests were performed to verify differences in EMG and pupil diameter measurements compared to the 3 s moment of stopping at the stop line after entering (Seg. R-2), moments of continuous short gap rejection (Seg. R-3), the moment of rejection (A.V(S)) or acceptance (A.V(C)) of a 4 s gap (Seg. R-4), subsequent moments of short gap rejection (Seg. R-5), and finally the moment of gap acceptance in the A.V(S) scenario (Seg. R-6). Similar segmentation was applied to highway merge sections and unsignalized intersections.