Modelling and simulation for biomechanical responses of acupuncture points based on multi-physics field coupling

Accurate acupoint localisation is essential for standardised acupuncture therapy and intelligent systems, yet remains challenging due to soft-tissue variability, ambiguous anatomical boundaries, and cross-domain distribution shifts.This study proposes a unified perception framework that integrates multiple complementary mechanisms within a single localisation pipeline.The proposed ACUR-Net incorporates high-resolution feature representation, geometry-aware relational modelling, uncertainty-aware regression, and domain adaptation to address anatomical variability and domain heterogeneity in a coordinated manner.The underlying assumption is that reliable acupoint localisation benefits from the joint modelling of anatomical topology, cross-domain feature alignment, and prediction uncertainty, rather than from isolated architectural modifications.Experiments conducted on the AcuSim-FAcupoint dataset show that the proposed framework achieves improved feature transfer stability and spatial consistency.Comparative evaluations indicate that ACUR-Net outperforms PFLD, PIPNet, HRNet, and ViTPose in terms of NME, PCK@0.05, and OKS-mAP.The results suggest that multi-factor integration is effective for enhancing localisation robustness under realistic conditions.