Deep Learning-Based Sitting Posture Recognition from Pressure Distribution Across Hard and Soft Seat Environment

This study investigates the classification of nine sitting postures using pressure distribution data from hard and soft seat surfaces. Three neural network architectures (FNN, CNN, ResNet) were evaluated under single-surface and mixed-domain training regimes. While all models achieved high accuracy (>96%) when trained on a mixed-domain dataset, significant performance degradation occurred in cross-domain testing. CNN demonstrated superior capability in leveraging spatial pressure features under mixed training conditions, while FNN exhibited relatively better cross-domain robustness. Results indicate that model performance highly depends on architectural inductive biases and training data diversity. These findings underscore the importance of employing representative multi-surface datasets for ensuring generalization in practical sitting posture recognition systems.