Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications

This study introduces a compact, machine learning (ML)-enhanced dual-band antenna designed specifically for wearable applications within Wireless Body Area Networks (WBANs). Wearable antennas in WBAN applications face challenges such as human body-induced electromagnetic interference, limited bandwidth, and SAR compliance, which hinder the effective performance of conventional designs. This work addresses these issues by employing machine learning (ML) to optimize the antenna design, thereby ensuring enhanced performance and adaptability in dynamic, on-body environments. The antenna is fabricated on a flexible 30 × 48.8 mm² Rogers Duroid 3003™ substrate, and operates efficiently at 2.4 GHz and 5.8 GHz, achieving fractional bandwidths of 9.7% and 7.8%, peak gains of 4.0 dBi and 6.2 dBi, and high radiation efficiencies of 91% and 93%, respectively. The radiation profile shows a bidirectional pattern along the E-plane, while the H-plane maintains nearly uniform radiation in all directions at both frequency bands. Compliance with safety regulations was confirmed through Specific Absorption Rate (SAR) analysis, with values of 1.17 W/kg (1 g) and 0.851 W/kg (10 g) at 2.4 GHz, and 0.813 W/kg (1 g) and 0.267 W/kg (10 g) at 5.8 GHz, all well below the regulatory thresholds set by FCC and ICNIRP. Mechanical flexibility and robustness were validated through testing under bent conditions on various body regions including the chest, arm, and lap, reflecting reliable operation in realistic WBAN use cases. Additionally, antenna resonant frequency was predicted using a supervised ML regression approach. Among the evaluated algorithms, the random forest model provided the best performance with an R² value of 87.70% and low error metrics (MAE: 0.35, MSE: 0.89, MSLE: 0.21, RMSLE: 0.35, RMSE: 0.94). These results confirm the antenna's reliability, safety, and adaptability for body-worn wireless systems.