Extensive efforts have been devoted to human gesture recognition with radio frequency (RF) signals. However, their performance degrades when applied to novel gesture classes that have never been seen in the training set. To handle unseen gestures, extra efforts are inevitable in terms of data collection and model retraining. In this article, we present XGest, a cross-label gesture recognition system that can accurately recognize gestures outside of the predefined gesture set with zero extra training effort. The key insight of XGest is to build a knowledge transfer framework between different gesture datasets. Specifically, we design a novel deep neural network to embed gestures into a high-dimensional Euclidean space. Several techniques are designed to tackle the spatial resolution limits imposed by RF hardware and the specular reflection effect of RF signals in this model. We implement XGest on a commodity mmWave device, and extensive experiments have demonstrated the significant recognition performance.

中文翻译：

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XGest：使用射频信号实现跨标签手势识别

大量的努力已经致力于利用射频 (RF) 信号进行人体手势识别。然而，当应用于训练集中从未见过的新手势类时，它们的性能会下降。为了处理看不见的手势，在数据收集和模型再训练方面不可避免地要付出额外的努力。在本文中，我们介绍了 XGest，这是一个跨标签手势识别系统，它可以准确地识别预定义手势集之外的手势，而无需额外的训练。XGest 的关键见解是在不同的手势数据集之间建立一个知识转移框架。具体来说，我们设计了一种新颖的深度神经网络，将手势嵌入到高维欧几里得空间中。设计了几种技术来解决该模型中 RF 硬件施加的空间分辨率限制和 RF 信号的镜面反射效应。我们在商用 mmWave 设备上实施 XGest，大量实验证明了显着的识别性能。