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

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 focus on verifying robustness on larger, diverse datasets. The FWIST approach offers promising applications in teleoperation, robot programming, drone control, and immersive VR/AR experiences.