A liquid metal dynamic wetting strategy for spatiotemporal monitoring of hand movements.

ABSTRACT: Current hand tracking technologies often suffer from inconsistent accuracy due to occlusion, electromagnetic interference, and ambiguous signals from soft sensors. These limitations hinder reliable motion capture for advanced human-machine interaction. To overcome them, we propose a liquid metal dynamic wetting-based flexible and wearable inertial skeletal tracking (FWIST) system. The system integrates 16 FWIST units on the hand to capture palm pitch/roll and finger bending angles in real time with high precision. It accurately reconstructs fingertip and joint trajectories, providing comprehensive hand motion analysis. In laboratory tests, a feedforward neural network achieved nearly 100% gesture recognition accuracy on a small dataset, proving the method's potential. Future work will focu

SUBMITTER: Zhan F 

PROVIDER: S-EPMC12774876 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature