Human dexterity relies on rapid, sub-second motor adjustments, yet capturing these high-frequency dynamics remains an enduring challenge in biomechanics and robotics. Existing motion capture paradigms are compromised by a trade-off between temporal resolution and visual occlusion, failing to record the fine-grained hand motion of fast, contact-rich manipulation. Here we introduce T-800, a high-bandwidth data glove system that achieves synchronized, full-hand motion tracking at 800 Hz. By integrating a novel broadcast-based synchronization mechanism with a mechanical stress isolation architecture, our system maintains sub-frame temporal alignment across 18 distributed inertial measurement units (IMUs) during extended, vigorous movements. We demonstrate that T-800 recovers fine-grained manipulation details previously lost to temporal undersampling. Our analysis reveals that human dexterity exhibits significantly high-frequency motion energy (>100 Hz) that was fundamentally inaccessible due to the Nyquist sampling limit imposed by previous hardware constraints. To validate the system's utility for robotic manipulation, we implement a kinematic retargeting algorithm that maps T-800's high-fidelity human gestures onto dexterous robotic hand models. This demonstrates that the high-frequency motion data can be accurately translated while respecting the kinematic constraints of robotic hands, providing the rich behavioral data necessary for training robust control policies in the future.

翻译：人类灵巧手依赖于快速、亚秒级的运动调节，然而捕捉这些高频动态仍是生物力学与机器人学领域的一项持久挑战。现有动作捕捉范式受限于时间分辨率与视觉遮挡之间的权衡，无法记录快速、接触密集操作中精细的手部运动。本文提出T-800——一种高带宽数据手套系统，能够以800赫兹频率实现同步全手运动追踪。通过融合创新的广播式同步机制与机械应力隔离架构，本系统在长时间剧烈运动过程中，确保18个分布式惯性测量单元（IMU）之间维持亚帧级时间对齐。我们证明，T-800能够恢复先前因时间欠采样而丢失的精细操作细节。分析显示，人类灵巧手展现出显著的高频运动能量（>100赫兹），这些能量因先前硬件约束导致的奈奎斯特采样极限而基本无法获取。为验证该系统在机器人操作中的实用性，我们实现了一种运动重定向算法，可将T-800捕获的高保真人手姿态映射至灵巧机器人手模型。研究表明，高频运动数据可在遵循机器人运动学约束的前提下被精确迁移，从而为未来训练鲁棒控制策略提供丰富的行为数据基础。