Hand exoskeletons are critical tools for dexterous teleoperation and immersive manipulation interfaces, but achieving accurate hand tracking remains a challenge due to user-specific anatomical variability and donning inconsistencies. These issues lead to kinematic misalignments that degrade tracking performance and limit applicability in precision tasks. We propose a subject-specific calibration framework for exoskeleton-based hand tracking that estimates virtual link parameters through residual-weighted optimization. A data-driven approach is introduced to empirically tune cost function weights using motion capture ground truth, enabling accurate and consistent calibration across users. Implemented on the Maestro hand exoskeleton with seven healthy participants, the method achieved substantial reductions in joint and fingertip tracking errors across diverse hand geometries. Qualitative visualizations using a Unity-based virtual hand further demonstrate improved motion fidelity. The proposed framework generalizes to exoskeletons with closed-loop kinematics and minimal sensing, laying the foundation for high-fidelity teleoperation and robot learning applications.

翻译：手部外骨骼是灵巧遥操作与沉浸式操控界面的关键工具，但由于用户个体解剖结构差异及穿戴不一致性，实现精确的手部追踪仍面临挑战。这些问题导致运动学失准，进而降低追踪性能并限制其在精密任务中的应用。本文提出一种面向特定受试者的外骨骼手部追踪标定框架，通过残差加权优化方法估计虚拟连杆参数。我们引入一种数据驱动方法，利用运动捕捉真值经验性地调整代价函数权重，从而实现对不同用户准确且一致的标定。该方法在Maestro手部外骨骼上对七名健康参与者进行验证，显著降低了不同手部几何形态下的关节及指尖追踪误差。基于Unity虚拟手模型的定性可视化结果进一步证明了运动保真度的提升。所提框架可推广至具有闭环运动学结构及最小传感配置的外骨骼系统，为高保真遥操作与机器人学习应用奠定基础。