Diff-MomentFormer: Generative Diffusion-Augmented Transformer for End-to-End Joint Moment Estimation.

Accurate estimation of human joint moments from multimodal sensor signals is essential for lower-limb exoskeleton control. Recent studies have addressed this problem in an end-to-end manner, but remain limited by insufficient long-range temporal modeling, limited training data, and class imbalance. To address these issues, we propose Diff-MomentFormer, a generative diffusion-augmented Transformer framework for end-to-end joint moment estimation from multimodal wearable sensor signals. The framework integrates a classifier-free conditional diffusion model for activity-conditioned synthetic data generation, together with a Transformer-based regression network for modeling long-range temporal dependencies and cross-modal interactions. Through the combination of controllable data augmentation and global temporal modeling, Diff-MomentFormer can learn more robust multimodal representations for accurate and stable joint moment estimation. Extensive experiments on a public lower-limb biomechanics dataset show that the proposed method consistently improves hip and knee joint moment estimation performance across different activity categories, while ablation studies further confirm the effectiveness of the proposed framework.