## 2 Materials and Methods
### Statistical Analysis  
#### 2.3.1 Statistical Analysis of Technical Factors  
A \(2^{k}\) factorial design was used with three factors: steering wheel inclination, steering wheel torque, and motor vibration frequency. Each factor has two levels: one labeled ergonomic and the other labeled high. For steering wheel inclination, 55 corresponds to the ergonomic level and \(65^{\circ}\) to the high level. For steering wheel torque, 3 represents the ergonomic level, and 6 Nm is the high level. Finally, for motor vibration frequency, 9.5 corresponds to the ergonomic level and 12 Hz to the high level. Since each of the three factors has two levels (\(k=3\)), the factorial design generates \(2^{3}=8\) experimental scenarios. The researchers labeled these experimental scenarios from P0 to P7. Table 1 shows how each factor varied between its two levels in each trial. For each of the 16 participants, the researchers randomly selected four of the eight available trials without repetition, ensuring that the trial with the ergonomic value P0 was included in all assignments (see Table 2). The Analysis of Variance (ANOVA) method was employed, and statistical significance was defined as \(p<0.05\). The dependent variable, called postures, represents the rotation angle of each body segment along each axis during the implementation of the protocol. The rotation angles are defined as follows: Rotations about the X-axis correspond to abduction-adduction movements, the Y-axis to flexion-extension movements, and the Z-axis to  
Figure 7: Mechanism for varying the steering wheel torque resistance. internal and external rotation movements. The analyzed body segments include the head, spine, arm, forearm, and thigh. On the other hand, the independent variables correspond to the technical factors: inclination, steering wheel torque, and cabin vibration frequency. The posture data were normalized using the Z-score method to compare the results obtained from the different participants. This process involves transforming the original data into a standard scale, where each value is expressed in terms of its distance from the mean, measured in standard deviation units. Thus, comparing postures becomes easier, regardless of differences in measurement units or data distribution, by applying Equation (1). \[Z=\frac{X-\mu}{\sigma} \tag{1}\]  
where \(Z\) represents the normalized value (Z-score), \(X\) is the original value of the variable, \(\mu\) is the mean of the variable, and \(\sigma\) is the standard deviation of the variable. A code developed in MATLAB 2024a(r) was used to process the data. The ANOVA method allowed for a cross-analysis of variables using the ANOVAN function. The code generated a matrix for each factor: one for the motor's vibration frequency, another for the steering wheel torque, and one more for the steering wheel inclination. Additionally, it included the subject variable. The code used binary categorical variables (0 and 1) for data recording. Table 2 shows that each participant performed four tests. Furthermore, according to Table 1, in each test (except P0), at least one factor reached a high value. The \begin{table}
\begin{tabular}{c c c c} \hline
**Trial** & **Inclination** & **Torque** & **Frequency** \\ \hline P0 & Ergonomic & Ergonomic & Ergonomic \\ P1 & Ergonomic & Ergonomic & High \\ P2 & Ergonomic & High & Ergonomic \\ P3 & High & Ergonomic & Ergonomic \\ P4 & Ergonomic & High & High \\ P5 & High & Ergonomic & High \\ P6 & High & High & Ergonomic \\ P7 & High & High & High \\ \hline \end{tabular}
\end{table} 
Table 1: Distribution of ergonomic and high values across the trials. Inclination refers to the steering wheel angle, Torque to the steering wheel resistance, and Frequency to the cabin vibration frequency.