Accurate classification of Radio-Frequency (RF) signals is essential for reliable wearable health-monitoring systems, providing awareness of the interference conditions in which medical protocols operate. In the overcrowded 2.4 GHz ISM band, however, identifying low-power transmissions from medical sensors is challenging due to strong co-channel interference and substantial power asymmetry with coexisting technologies. This work introduces the first open source framework for automatic recognition of SmartBAN signals in Body Area Networks (BANs). The framework combines a synthetic dataset of simulated signals with real RF acquisitions obtained through Software-Defined Radios (SDRs), enabling both controlled and realistic evaluation. Deep convolutional neural networks based on ResNet encoders and U-Net decoders with attention mechanisms are trained and assessed across diverse propagation conditions. The proposed approach achieves over 90% accuracy on synthetic datasets and demonstrates consistent performance on real over-the-air spectrograms. By enabling reliable SmartBAN signal recognition in dense spectral environments, this framework supports interferenceaware coexistence strategies and improves the dependability of wearable healthcare systems.

翻译：射频信号的准确分类对于可靠的穿戴式健康监测系统至关重要，它能够提供医疗协议运行时所处干扰条件的感知。然而，在拥挤的2.4 GHz ISM频段中，由于强烈的同信道干扰以及与共存技术之间存在显著的功率不对称性，识别来自医疗传感器的低功率传输具有挑战性。本研究首次提出了一个用于在体域网中自动识别SmartBAN信号的开源框架。该框架将模拟信号的合成数据集与通过软件定义无线电获取的真实射频采集数据相结合，从而支持受控和逼真的评估。基于ResNet编码器和带有注意力机制的U-Net解码器的深度卷积神经网络，在多种传播条件下进行了训练和评估。所提出的方法在合成数据集上实现了超过90%的准确率，并在真实空中频谱图上表现出一致的性能。通过在密集频谱环境中实现可靠的SmartBAN信号识别，该框架支持干扰感知的共存策略，并提高了穿戴式医疗系统的可靠性。