Deep learning-powered composite foam smart glove with cross-dimensional conductive networks for sign language recognition

The development of wearable sensor-based sign language recognition systems has become a solution to facilitate effective communication among hearing-impaired groups, but achieving high integration, sign language standardization, and anti-environmental interference remains challenging. Here, we design a smart glove system for real-time sign language interpretation based on a composite foam with a cross-dimensional conductive network, integrating flexible switches, pressure sensors, custom miniaturized circuits, and deep learning modules. The sensor exhibits electromagnetic shielding, thermal management, and antibacterial capabilities, enhancing the smart glove's adaptability to the external environment. The elastic conductive framework of the foam allows the system to realize the start/stop function and fast response to gestures. In addition, a deep learning model of multi-component collaboration and mechanism fusion is constructed, along with the establishment of a comprehensive set of sign language rules, which realizes 99.4 % accurate recognition of 26 letters through only three pressure sensors. Overall, our proposed strategy provides a new way for smart gloves to work stably in harsh environments, and is expected to eliminate communication barriers among hearing-impaired groups due to sign language diversity and cultural differences.