School of Computing Technologies, RMIT University, 124 La Trobe Street, Melbourne, VIC 3000, Australia.
School of Computing, Macquarie University, 4 Research Park Drive, North Ryde, NSW 2109, Australia.
Sensors (Basel). 2024 Mar 20;24(6):1978. doi: 10.3390/s24061978.
Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of 95.17% on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost.
射频（RF）技术已被应用于实现人机交互中的高级行为感应。由于其无需设备的感应能力以及在物联网设备中的广泛可用性，能够实现高精度的基于手指手势的识别具有挑战性，因为 RF 信号分辨率低且用户异质性大。在本文中，我们提出了 MeshID，这是一种新颖的基于 RF 的用户识别方案，可通过高精度的手指手势进行识别。MeshID 显著提高了对 RF 信号干扰的感应灵敏度，因此能够通过速度分布分析（VDP）特征从不太明显的手指运动中提取微妙的个体生物特征，例如在空中绘制数字。我们设计了一种基于第一组件反向模块的高效少样本模型再训练框架，在复杂环境中实现了高模型鲁棒性和性能。我们进行了全面的真实世界实验，结果表明，MeshID 在三种室内环境中的平均用户识别准确率达到 95.17%。结果表明，MeshID 在识别性能上优于最先进的方法，成本更低。
