Beyond Gestures: Estimating Full Hand Pose and Contact Forces from Wrist-Worn Pressure Sensor Array
arXiv:2609.16518v1 Announce Type: cross Abstract: 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
Overview
arXiv:2609.16518v1 Announce Type: cross Abstract: 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.
Source
Originally published at arxiv.org.
Related Articles
- Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity
- MR-GLi: Mixed Reality-Based Gripper-Linked Overlays for Underwater Robot Arm Teleoperation via Bilateral Control
- Auto-HSI: Personalized human control of a robot swarm on demand by using LLMs for online automatic code generation
Source: https://arxiv.org/abs/2609.16518