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

2026-01-22

AI Summary

This study addresses the challenge of reliably identifying low-power Smart Body Area Network (SmartBAN) signals in the 2.4 GHz ISM band under dense interference and power asymmetry, which severely limits the performance of wearable health monitoring systems. To tackle this issue, the work proposes the first open-source signal recognition framework tailored for SmartBAN, integrating synthetically generated data with real over-the-air radio frequency measurements acquired via software-defined radio (SDR). The framework employs a novel hybrid ResNet-U-Net architecture augmented with an attention mechanism to enable interference-aware, high-accuracy signal classification. Experimental results demonstrate that the proposed method achieves over 90% accuracy on synthetic datasets and maintains robust performance on real-world spectrograms, significantly enhancing system reliability in complex electromagnetic environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• RF signal classification
• SmartBAN
• ISM band
• co-channel interference
• wearable healthcare

Innovation

• SmartBAN
• RF signal classification
• deep convolutional neural networks
• software-defined radio
• ISM band interference