A soft closed-loop glove with gesture sensing and haptic feedback for real-time human-machine interfaces

Human-machine interfaces (HMIs) are essential tools facilitating effective interactions between users and intelligent systems. However, current wearable HMIs, particularly smart gloves, often face challenges in precisely recognizing subtle finger movements and delivering real-time tactile feedback, limiting their operational effectiveness and user immersion. To overcome these challenges, we propose an intelligent glove system integrating highly stretchable liquid metal-based strain sensors and programmable electromagnetic feedback actuators to realize closed-loop immersive human-machine interaction. The strain sensors achieve superior stretchability (over 200%), linear response, minimal hysteresis, and robust durability, effectively capturing complex hand movements. Gesture recognition, performed by a convolutional neural network (CNN), achieves an accuracy of 98% across common gestures, demonstrating exceptional precision and robustness. Furthermore, the glove incorporates the soft programmable electromagnetic actuators with an output force greater than 0.25 N offering adjustable, precise tactile feedback through pulse-width modulation, enables users to intuitively perceive tactile sensations in human machine interaction scenarios involving robotic manipulation and motion guidance tasks. Comprehensive experimental results confirm the intelligent glove system’s superior real-time responsiveness and adaptability in dynamic environments. This advanced closed-loop HMI glove significantly enhances operator immersion, operational accuracy, and interaction realism, showing great promise in diverse fields including robotics, virtual reality, and remote teleoperation.