Development and Validation of a Dynamic Multi-Modal Digital Human Hand Model for Ergonomic Design of Handheld Products

Abstract

Handheld interactive products require device geometries that accommodate coupled skeletal motion and nonlinear soft-tissue deformation during grasping and pressing. However, many existing digital hand models prioritize either kinematics or surface geometry and therefore provide limited predictive power for contact mechanics and deformation under realistic interactions. This study developed a Dynamic Digital Human Hand Model (DHHM) that integrates (i) percentile-based hand surface models derived from 3D scans of 864 Chinese adults, (ii) layered internal anatomy (bone, muscle, connective tissue, fat, and skin) refined using anatomical atlases and public anatomical datasets, (iii) inverse-kinematics-driven motion control, and (iv) finite element-based soft-tissue simulation implemented in Ziva VFX using a second-order implicit backward differentiation formula (BDF2) integrator. The P50 model was validated using motion capture, 3D surface scanning, and surface electromyography (sEMG) from four participants whose hand dimensions were close to the P50 hand (maximum absolute deviation 2.0 mm across eight key measures). Participants performed seven representative grasp/press tasks involving a mouse, a laparoscopic ultrasonic scalpel, a surgical needle holder, and a smartphone. Across tasks, the DHHM reproduced primary contact patterns observed in scanned postures. Surface deviation analysis showed high agreement between simulated and scanned skin surfaces within an industrial tolerance (maximum deviation within ±1.655 mm). Predicted activation trends of thenar, hypothenar, and extensor muscle groups were qualitatively consistent with sEMG across task types. The proposed DHHM provides a validated pathway for high-fidelity hand-product interaction simulation to support ergonomic design with reduced reliance on physical prototypes.

Keywords: Digital Human Modeling, Hand Biomechanics, Soft-tissue Simulation, Inverse Kinematics, Surface Electromyography

DOI: 10.54941/ahfe1007445