This article studies the problem of RFID-based gesture recognition, which is practically important in various human-computer interaction scenarios, for example, smart homes, intelligent logistics, and smart cities. However, the existing solutions normally suffer from two major limitations: the model-driven methods are sensitive to specific environmental factors, and usually do not adapt well to a complex scenario that is full of multipath; the data-driven methods normally need the collection of massive RFID training data, and deploying the model in the remote cloud leads to long response delay. To overcome the above limitations, this article proposes a cross-domain augmentation-based AI learning (CAL) framework in the context of cloud-edge computing. In the CAL framework, we can simulate massive RFID phase profiles by converting the computer vision data that contains the gesture movement information, instead of costing lots of manpower to actually collect RFID training data. The simulated RFID phase profiles are used to train an AI model in the high-performance cloud. Note that since many sources of this kind of computer vision data are available online, we actually do not even need any manpower to collect training data. To achieve time-efficient recognition, knowledge distillation is applied to get a light and accurate model, which is deployed at the edge side. Thus, recognition response delay can be significantly reduced because the edge server where the AI model is actually deployed is much closer to users than the cloud server. We use commercial off-the-shelf RFID, Kinect, a high-performance server, and a laptop to implement the CAL framework. Extensive experiments are conducted to evaluate the performance of CAL. The results reveal that gesture recognition accuracy of CAL can reach nearly 90 percent without collection of any RFID training data.

中文翻译：

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基于跨域增强的网络内手势识别人工智能学习框架

本文研究了基于 RFID 的手势识别问题，该问题在各种人机交互场景中具有重要的实际意义，例如智能家居、智能物流和智慧城市。然而，现有的解决方案通常存在两大局限性：模型驱动的方法对特定环境因素敏感，通常不能很好地适应充满多径的复杂场景；数据驱动的方法通常需要收集海量的RFID训练数据，将模型部署在远程云端导致响应延迟较长。为了克服上述限制，本文提出了一种基于云边缘计算的跨域增强型 AI 学习（CAL）框架。在 CAL 框架中，我们可以通过转换包含手势运动信息的计算机视觉数据来模拟海量的 RFID 相位轮廓，而不是花费大量人力来实际收集 RFID 训练数据。模拟的 RFID 相位配置文件用于在高性能云中训练 AI 模型。请注意，由于此类计算机视觉数据的许多来源都可以在线获得，因此我们实际上甚至不需要任何人力来收集训练数据。为了实现省时的识别，应用知识蒸馏来获得轻量且准确的模型，并将其部署在边缘侧。因此，可以显着减少识别响应延迟，因为实际部署 AI 模型的边缘服务器比云服务器更接近用户。我们使用商用现成的 RFID、Kinect、高性能服务器、和一台笔记本电脑来实现 CAL 框架。进行了大量实验来评估 CAL 的性能。结果表明，在不收集任何RFID训练数据的情况下，CAL的手势识别准确率可以达到近90%。