Predictive model of tractor driving posture considering front and rear view

Recently, tractor research and development has focused on ergonomic performance design and proposed improved requirements for driving comfort and safety. The digital human model is important for virtual ergonomics design because whether or not the driving posture of the model matches reality significantly influences the design results. Because of the poor accuracy and integrity of predictions for tractor driving posture, simulations for the front and rear driving postures of high-power tractors were analysed. Fifteen dependent variables were determined by simplifying driving posture according to the representative and measurability of the joint angle of the human body and the effective segment size. The data from 54 subjects in the forward driving and backward working postures were measured using a portable three-coordinate measuring machine. The key factors influencing each dependent variable were determined through comprehensive correlation, scatter plot, and linear regression analyses. Correlation analysis of the dependent variables revealed that regression of the dependent variable was arranged in descending order. The regression prediction model of each dependent variable was established by analysing the lower-level regression variables while introducing strong correlation and high-level variables into the model. To verify the accuracy and rationality of the model, statistical tests and experimental verification were carried out, respectively. The model was accurate (p