3.2. Indicator Screening Based on LASSO Regression
The data were centrally normalized to eliminate the effect of different index magnitudes [
40]. Using Lasso regression, the selected gender, height, weight, back object pressure, back peak object pressure, back peak contact pressure, back peak strength, back contact area, back contact pressure, back strength, hip object pressure, hip peak object pressure, hip peak contact pressure, hip peak strength, hip contact area, hip contact pressure, hip strength, and another 17 indicators were used for parameter estimation and variable selection, with the help of Statuses 15 software. The LASSO regression coefficients are shown in
Table 2.
Through the parameter estimation results of Lasso regression, gender, height and weight were selected according to the model optimum, four variables of back indexes, back peak object pressure, back peak contact pressure, back contact area and back contact pressure; two variables of hip indexes, hip contact area and hip strength, and eight variables that were not relevant to the prediction of comfort were excluded.
3.3. Data Analysis
Using IBM SPSS Statistics 20 software, the collected backrest and seat pressure data were subjected to a Kolmogorov–Smirnov normal distribution test with several related samples, such as subjective rating data. After the test, both backrest and seat pressure data were found to not obey the normal distribution situation. Therefore, this paper selected a non-parametric test to analyze the data, using the Friedman test for the mean and standard deviation of the three backrest tilt angles under the two pitches. The pressure distribution and two posterior comparisons for a seat and pedal pitch of 63 cm are shown in
Table 3 and
Table 4.
The body pressure distribution reflects the contact effect between the human body and the seat, which can better characterize the body pressure distribution characteristics. As shown in
Table 3, when the distance between the seat and the footrest is 63 cm, the peak back object pressure is the highest when the backrest angle is 110°, at 32.81 ± 1.67 kPa; the peak back object pressure is not much different from that at 110° when the backrest is 120°, at 32.03 ± 2.16 kPa; the peak object pressure is the lowest when the backrest angle is 100°, at 30.71 ± 2.15 kPa. Back peak object pressure is the highest point or peak in the pressure distribution of the human back; the human body in different sitting, standing, or lying postures is subjected to different pressures. Measurement and analysis of the back peak object pressure can help to understand the impact of different backrest angles on the back force, especially the pressure distribution on the spine and muscles. Usually, reducing the peak object pressure on the back helps to improve the comfort of the human sitting posture. As shown in
Table 4, a post hoc two-by-two comparison of the three backrest inclination angles at 63 cm spacing showed significant differences in peak back object pressure and peak hip object pressure for each of the two different angles at the 95% confidence interval.
As shown in
Table 3, the peak contact pressure on the back at the seat and footrest distance of 63 cm and backrest tilt angle of 120° is the largest, 34.55 ± 2.05 kPa; the peak contact pressure at 100° is the smallest, 31.13 ± 2.13 kPa; at 110°, peak contact pressure is in the middle, 33.98 ± 2.14 kPa. Peak contact pressure is the pressure on the seat cushion and backrest. The average of the four values adjacent to the maximum value is given by this indicator, which reduces the pressure cushion surface wrinkles caused by the abnormally excessive pressure on the results. In general, the peak contact pressure shows a correlation with the subjective evaluation of overall comfort, and the larger the peak contact area, the lower the subjective comfort evaluation [
35]. As shown in
Table 4, a post hoc two-by-two comparison of the three backrest inclination angles at 63 cm pitch showed significant differences in peak back-contact pressure and peak hip-contact pressure at 95% confidence intervals for each of the two different angles.
As shown in
Table 3, the backrest is subjected to the largest contact area of 741.99 ± 7.76 cm
2 at an inclination of 120°, followed by 718.98 ± 6.48 cm
2 at an inclination of 110° and 679.19 ± 5.52 cm
2 at an inclination of 120°. The contact area of the buttocks is the opposite of that of the back, with the largest contact area of 741.99 ± 7.76 cm
2 at an inclination of 100°, followed by 718.98 ± 6.48 cm
2 at an inclination of 110°, and 679.19 ± 5.52 cm
2 at an inclination of 120°. The contact area is the total area of the back or buttocks in contact with the support, and the size of this area can be used to measure the extent and condition of the body part that is in contact. Generally speaking, the larger the contact area, the lower the local pressure on the back and the better the fit of the hips to the seat. This means that the corresponding average pressure will be smaller, and comfort will be increased when using certain seat materials. However, under actual flight conditions, a too-large backrest inclination angle will affect the view angle when driving, so usually the backrest inclination angle is not too large. As shown in
Table 4, a two-by-two post hoc comparison of the three backrest inclination angles at 63 cm showed significant differences in back contact area and hips at the 95% confidence interval for each of the two different angles.
There are significant differences in contact pressure for different backrest inclination angles, where 120° inclination (13.29 ± 0.29 kPa) > 100° inclination (13.13 ± 0.18 kPa) > 110° inclination (13.01 ± 0.14 kPa). Usually, reducing the discomfort caused by contact pressure can improve the comfort during human contact with the support surface, resulting in less irritation being felt by the human body. As shown in
Table 4, a post hoc two-by-two comparison of the three backrest inclination angles at 63 cm showed significant differences in back-contact pressure and hip-contact pressure at 95% confidence intervals for each of the two different angles.
Hip strength refers to the strength of the force or pressure exerted in the hip region of the human body. For buttock strength, the greatest strength was found at an inclination angle of 100° at 977.39 ± 20.96 kPa, followed by 934.55 ± 17.67 kPa at an inclination angle of 110° and 902.32 ± 18.01 kPa at an inclination angle of 120°. Usually, a smaller buttock strength helps to reduce local discomfort and avoid pressure concentration on the skin and tissues. As shown in
Table 4, a post hoc two-by-two comparison of the three backrest inclination angles at 63 cm showed significant differences in hip strength at 95% confidence intervals for each of the two different angles.
The significance of the pressure distribution when the seat and pedal pitch are 63 cm and a post-hoc two-by-two comparison are shown in
Table 3 and
Table 4.
As shown in
Table 5, the peak back-object pressure was the highest at 36.66 ± 1.89 kPa at a tilt angle of 100°, followed by 36.17 ± 1.93 kPa at a tilt angle of 110° and 31.23 ± 1.48 kPa at a tilt angle of 120° for a seat and footrest pitch of 68 cm. As shown in
Table 6, for the three backrest tilt angles at 68 cm pitch, a post hoc two-by-two comparison was conducted, and the peak back-object pressures for each of the two different angles were significantly different at the 95% confidence interval.
As shown in
Table 5, the peak back-contact pressure at a seat-to-footrest pitch of 68 cm was the highest at an inclination angle of 100°, at 38.82 ± 2.01 kPa; followed by 37.75 ± 1.52 kPa at an inclination angle of 110°; and the lowest at an inclination angle of 120°, at 32.31 ± 1.52 kPa. As shown in
Table 6, the peak back-contact pressure for a post hoc two-by-two comparison of the three backrest inclination angles at 68 cm spacing showed that the peak back-contact pressure at each of the two different angles was significantly different at the 95% confidence interval.
At a distance of 68 cm, the back-contact area was the largest at 120° backrest inclination, at 479.41 ± 13.29 cm
2; the smallest at 100° back-contact area, 392.11 ± 8.76 cm
2; and in the middle at 110° inclination, at 410.83 ± 11.04 cm
2. Hip contact pressure is also the opposite of the back-contact pressure and is greatest when the tilt angle is 110° at 745.07 ± 10.23 cm
2, followed by 730.21 ± 6.32 cm
2 at 110° and 679.49 ± 8.32 cm
2 at 120°. When the same seat material is used, a large contact area represents a better experience being measured. As above, for the actual driving conditions, a larger recline angle has an impact on the driving view, resulting in a restricted viewable area, so a larger recline angle is usually unsuitable for driving conditions. As shown in
Table 6, a post hoc two-by-two comparison of the three recline angles at 68 cm spacing showed that the back-contact area at each of the two different angles was significantly different at the 95% confidence interval.
As shown in
Table 5, for back-contact pressure at a spacing of 68 cm, the backrest inclination angle was 100° (14.58 ± 0.47 kPa) > inclination angle 110° (13.85 ± 0.29 kPa) > inclination angle 120° (13.53 ± 0.21 kPa) for back-contact pressure. As shown in
Table 6, post hoc two-by-two comparisons of the three backrest inclination angles at 68 cm spacing showed significant differences in back-contact pressure and hip-contact pressure at 95% confidence intervals for each of the two different angles.
With a 68 cm pitch, the hip strength was greatest at a tilt angle of 110°, at 982.31 ± 20.22 kPa, and was similarly low at a tilt angle of 100°, at 982.31 ± 20.22 kPa, and had a tilt angle of at least 120° at 926.33 ± 19.46 kPa. As shown in
Table 6, for the 68 cm pitch, the post hoc two-by-two comparison of the three backrest inclination angles under 68 cm spacing showed significant differences in hip strength at the 95% confidence interval for each of the two different angles.
3.4. Establish the IPSO-SVR Prediction Model
From the final 162 screened sets of data, 130 sets were selected as the training sample set, and the remaining 32 sets were used as the validation sample set. To quantify the accuracy of the prediction model, root mean square error (RMSE), mean absolute error (MAE), and goodness of fit (R
2) were applied as evaluation metrics in this paper. Usually, RMSE can be used to measure the deviation between the test value and the true value, which is a common index of prediction accuracy; MAE can avoid the problem of errors cancelling each other, accurately reflecting the size of the actual prediction error; R
2 can verify the extent to which the regression model fits the data and effectively prove the extent to which the validation set can be predicted by the training set. Nine variables, including gender, height, weight, back peak object pressure, back peak contact pressure, back-contact area, back-contact pressure, hip-contact area and hip strength, were set as the input layer, and the subjective evaluation score was used as the output layer. The improved particle-swarm-algorithm-optimized SVR was trained using 130 sets of training sample data to obtain the optimal parameters, the relative errors in the prediction model for the predicted and true values are shown in
Table 7, and the prediction results of the established prediction model, IPSO-SVR, are shown in
Figure 6.
The prediction results show that the back-prediction accuracy of IPSO-SVM is 94.00%, the root mean square error (RMSE) is 0.37, the mean absolute value error (MAE) is 0.32, and the goodness-of-fit (R2) is 0.92, indicating that optimizing the relevant parameters of the support vector machine using the improved particle swarm algorithm has obvious advantages in predicting comfort.
3.5. Model Validation
To further verify the prediction accuracy and stability of the improved particle swarm algorithm for support vector machine regression, the genetic algorithm optimized support vector machine regression (GA-SVR), particle swarm algorithm optimized support vector machine regression (PSO-SVR), and traditional support vector machine regression (SVR) were compared and verified, as shown in
Figure 7.
According to
Figure 6, SVR has the highest number of iterations and a large degree of adaptation, meaning that it is not a good choice. GA-SVR, although it shows some improvements over the traditional SVR algorithm in terms of the number of iterations and degree of adaptation, is still not ideal, and PSO-SVR is significantly better. IPSO-SVR ensures that the degree of adaptation is small enough while still satisfying the need for a faster convergence rate. The results show that the IPSO algorithm has a better ability to find the global optimal solution.
As can be seen from
Table 8, although PSO-SVR is superior in terms of fit, it is still not as stable as the improved particle swarm algorithm, and the prediction effect is not as good as the improved particle swarm algorithm; IPSO-SVR has the highest prediction accuracy and the best model stability, 94.00% and 8.71%, respectively, and the fit superiority R
2 is also higher in comparison; the prediction performance and fit effect of SVR are relatively poor, and the model is not stable, with the highest relative standard deviation (RSD) of 16.56. Generally speaking, the smaller the value of RSD, the lower the degree of dispersion of the data, and the higher the inter-data consistency and stability [
41]. Therefore, by combining the calculation of different evaluation parameters, it is verified that IPSO has a good optimization effect on two important parameters of SVR and has a certain research value, indicating that the model has good stability. Comparing the prediction of aircraft seat comfort using artificial neural networks by Zhao [
19] and others, the RMSE and R
2 obtained in the literature were 1.21 and 78%, respectively, and it was found that, in the model using IPSO to optimize the SVR, the data were better in terms of RMSE and prediction accuracy, which indicates the superior prediction accuracy of this method. The related research on the predictive analysis of airline seat comfort is still limited compared to other industries and needs to be further explored.