A Fuzzy-Encoded Dual-Modal Soft Glove for Gesture and Grasping Object Classification

Human–machine interaction technologies are crucial for enhancing human capabilities in the digital world. The hand, as a primary interaction tool, conveys information through gestures and tactile signals. With advancements in flexible electronics and fuzzy systems, it is now possible to achieve interpretable and accurate interactions using hand gestures and tactile information. This article introduces a fuzzy-encoded, dual-modal soft glove applied to gesture and grasping object classification. The glove, featuring 10 soft pressure sensors and 6 soft bending sensors, is compactly designed and exhibits excellent sensitivity, with average sensitivities of 133 N$^{-1}$ for soft pressure sensors and 13°$^{-1}$ for soft bending sensors. The fuzzy Mamba encoding classification algorithm, inspired by the interpretability and uncertainty resilience of fuzzy systems and the fitting ability of state-space models, was developed. To the best of authors’ knowledge, this work is the first to combine the Mamba structure with fuzzy intelligent systems. Applied to gesture and grasping object classifications, the glove achieved average accuracies of 96.3% and 94.9%, respectively. The fuzzy encoding leverages membership vectors provided by experts to enhance interpretability. These results demonstrate the glove’s effectiveness in signal recording and processing and highlight the powerful synergy between flexible electronic technologies and fuzzy systems.