With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.

中文翻译：

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Widar3.0：使用 Wi-Fi 的零努力跨域手势识别。

随着信号处理技术的发展，无处不在的 Wi-Fi 设备为通过从无线信号中学习运动表示来解决具有挑战性的人体手势识别问题提供了前所未有的机会。基于 Wi-Fi 的手势识别系统虽然在特定数据域上产生了良好的性能，但如果没有对新域的明确适应努力，实际上仍然难以使用。已经提出了各种开创性的方法来解决这一矛盾，但是当出现新的数据域时，数据收集或模型重新训练仍然需要额外的训练工作。为了推进跨域识别并实现完全零努力识别，我们提出了 Widar3.0，一种基于 Wi-Fi 的零努力跨域手势识别系统。Widar3 的关键见解。0 是在较低的信号水平上推导和提取人类手势的域无关特征，这些特征代表手势的独特动力学特征并且与域无关。在此基础上，我们开发了一种只需要一次性训练但可以适应不同数据域的通用模型。对各种领域因素（即环境、位置和人的方向）的实验表明，在没有模型重新训练的情况下，域内识别的准确率为 92.7%，跨域识别的准确率为 82.6%-92.4%，优于 state-of-最先进的解决方案。我们开发了一种万能的通用模型，它只需要一次性训练，但可以适应不同的数据领域。对各种领域因素（即环境、位置和人的方向）的实验表明，在没有模型重新训练的情况下，域内识别的准确率为 92.7%，跨域识别的准确率为 82.6%-92.4%，优于 state-of-最先进的解决方案。我们开发了一种万能的通用模型，它只需要一次性训练，但可以适应不同的数据领域。对各种领域因素（即环境、位置和人的方向）的实验表明，在没有模型重新训练的情况下，域内识别的准确率为 92.7%，跨域识别的准确率为 82.6%-92.4%，优于 state-of-最先进的解决方案。