MXene/graphene composite flexible impedance glove with wide linear range for cross-subject gesture recognition

With the continuous evolution of human-machine interaction (HMI) technologies, natural and intuitive gesture recognition has emerged as a pivotal interaction modality. However, constrained by inter-individual differences in hand dimensions and wearing habits, existing gesture recognition techniques still suffer from limited generalization performance in cross-subject recognition. This paper proposes a flexible impedance glove based on MXene/graphene composites with wide linear response range, which enables lightweight and zero-shot cross-subject gesture recognition. The sensing layer of the glove is fabricated using MXene/graphene/spandex composites, delivering a wide linear tensile range of 0-90% (R² = 0.995), and exceptional dual-modal sensing capability, effectively suppressing baseline fluctuations induced by individual differences. To enable high-precision gesture signal acquisition and intelligent classification, a self-designed miniaturized, low-cost impedance measurement module and the Random Forest (RF) algorithm were integrated. The customized hardware module achieves a high signal-to-noise ratio (SNR) of 61.6 dB and an average measurement accuracy of 99.79%. By employing a computationally lightweight Random Forest (RF) algorithm, the integrated system delivers an intra-subject recognition accuracy of 99.43% in classifying 12 complex gestures. Remarkably, it achieves a zero-shot cross-subject recognition accuracy of 91.04%, which further improved to 97.85% with the introduction of merely 5% individual calibration data. Furthermore, the successfully developed applications in intelligent sign language translation and low-latency game control demonstrate the promising application potential of the proposed system in barrier-free communication, HMI and other related fields.