Immersive communications rely on smart perception based on diversified and augmented sensing and feedback technologies. However, the increasing of functional components also raises the issue of increased system complexity. Here, we propose a modular soft glove with multimodal sensing and feedback functions by exploring and utilizing the multiple properties of glove materials. With a single design of basic structure, the main functional unit possesses triboelectric-based sensing of static and dynamic contact, vibration, strain, and pneumatic actuation. Additionally, the same unit is also capable of offering pneumatic tactile haptic feedback and electroresistive thermal haptic feedback. Together with a machine learning algorithm, the proposed glove not only performs real-time detection of dexterous hand motion and direct feedback but also realizes intelligent object recognition and augmented feedback, which significantly enhance the communication and perception of more comprehensive information. In general, this glove utilizes a facile designed sensing and feedback device to achieve dual-way and multimodal communication among humans, machines, and the virtual world via smart perceptions.
Keywords: augmented feedback; machine learning; modular glove; multimodal haptic feedback; tactile sensing; triboelectric.