发表机构
Meta Reality Labs Research（Meta现实实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种腕戴式压力传感腕带，利用肌肉收缩和肌腱位移产生的压力模式，通过循环网络估计全手姿态与接触力，实验显示手指关节MAE为4.6度，接触力R²达0.57，可补充视觉盲区。
AI 中文摘要
捕捉手部运动和交互力对于交互式计算、虚拟现实以及机器人学习的高保真触觉演示至关重要。我们介绍了一种腕戴式压力传感腕带，它能在单个可穿戴设备上恢复连续的全手姿态和分布接触力。该系统由围绕手腕的柔性电容传感器阵列组成，无需电皮肤接触，以及一个循环网络，将产生的压力信号映射到手部状态。我们的关键洞见是，肌肉收缩和肌腱位移会产生压力模式，这些模式与手部姿态和交互力密切相关。为验证这一点，我们收集了腕部压力、光学动作捕捉手部姿态和触觉手套交互力的同步记录，涵盖了孤立手指运动、指尖力压力测试和自然手物操作。在孤立的单用户运动上，腕带达到了平均手指关节MAE 4.6度，在四名用户操作日常物品时，它估计每指接触力的R²=0.57，外部姿态信号可将其提升至0.75。我们将腕带视为日常可穿戴设备星座中的一个节点——例如与第一人称视角相机配对——增加了视觉无法观察到的接触力，并在手部被遮挡时接管。
英文摘要
Capturing hand motion and interaction forces is critical for interactive computing, VR, and high-fidelity tactile demonstrations for robot learning. We introduce a wrist-worn pressure-sensing wristband that recovers continuous full-hand pose and distributed contact force on a single wearable. The system consists of flexible capacitive sensor arrays around the wrist, which require no electrical skin contact, and a recurrent network that maps the resulting pressure signal to hand state. Our key insight is that muscle contraction and tendon displacement produce pressure patterns, which correlate strongly with hand pose and interaction force. To validate this, we collect synchronized recordings of wrist pressure, optical motion-capture hand pose, and tactile-glove interaction force, covering isolated finger motion, fingertip-force stress tests, and natural hand-object manipulation. On isolated single-user motion the wristband attains $4.6^\circ$ mean finger-joint MAE, and across four users manipulating everyday objects it estimates per-finger contact force at $R^2=0.57$, which an external pose signal brings up to $0.75$. We see the wristband as one node in a constellation of everyday wearables -- e.g. paired with an egocentric camera -- adding the contact force that vision cannot observe and taking over when the hand is occluded.
