RF Intelligence for Health: Classification of SmartBAN Signals in overcrowded ISM band

TL;DR

This paper presents an open source framework utilizing deep learning to accurately classify SmartBAN signals in crowded 2.4 GHz ISM bands, enhancing interference management for wearable health systems.

Contribution

It introduces the first open source framework combining synthetic and real RF data with advanced neural networks for SmartBAN signal classification in dense spectral environments.

Findings

Achieves over 90% accuracy on synthetic datasets

Demonstrates consistent performance on real RF spectrograms

Supports interference-aware coexistence in wearable healthcare systems

Abstract

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.