Research on Predicting Occupant Height Using CNN-Based Pressure and Posture in Zero-Gravity Seats

Zero-gravity seats alleviate prolonged sitting fatigue by optimizing human body pressure distribution, but the correlation mechanism between body size parameters and pressure distribution remains unclear. This study proposes a deep learning model based on multimodal data fusion, combining pressure matrices and postural angle data to construct a convolutional neural network (CNN) with a height prediction error ⩽3 cm. Experiments collected pressure and posture data from 100 participants with diverse anthropometric percentiles. Through the fusion of features and the optimization of the model, the study managed to quantify how height and weight impact pressure gradients. The results indicate that the model achieved a prediction R² value of 0.73, which confirms that there is a strong correlation between pressure distribution and body size parameters. The findings offer theoretical and technical support for the adaptive adjustment systems within intelligent cabins.