Edge-Deployable Neural Network Framework for Real-Time Antenna Performance Prediction in Wearable Telemedicine Systems

We demonstrate a lightweight neural network framework for antenna performance prediction in wearable telemedicine systems. Applied to a Sierpinski gasket fractal antenna operating across 1.5–46.6 GHz, our multilayer perceptron architecture achieves 96% prediction accuracy on validation data and 86% R² on experimental test measurements. The framework combines RF chamber measurement data with data augmentation techniques (noise addition and cubic interpolation) to train a 2-64-32-3 MLP architecture. We convert the trained model to TensorFlow Lite format (280KB compressed size) to enable potential deployment on ARM-based edge devices. Experimental validation includes measurements at multiple angular orientations (0°, ±20°, ±45°, ±70°, ±90°, 180°) across the frequency range. The antenna achieves impedance matching (S11: –19.7 to –51.6 dB) across Sub-6 GHz, Mid-band, and millimete-rwave frequencies using cost-effective FR4 substrate. This work demonstrates the feasibility of applying standard machine learning techniques to antenna performance prediction for medical wearable applications, establishing a foundation for future integration with adaptive communication systems.