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

Aug 5, 2026

A compact, machine learning-enhanced dual-band wearable antenna is developed for Wireless Body Area Networks (WBANs), targeting key limitations of conventional designs caused by human-body electromagnetic interference, limited bandwidth, and SAR compliance requirements. Fabricated on a flexible 30 × 48.8 mm² Rogers Duroid 3003™ substrate, it operates efficiently at 2.4 GHz and 5.8 GHz with fractional bandwidths of 9.7% and 7.8%, peak gains of 4.0 dBi and 6.2 dBi, and radiation efficiencies of 91% and 93%. SAR analysis confirms safe exposure well below FCC and ICNIRP thresholds, while bending tests on chest, arm, and lap validate robustness and reliable on-body operation. A supervised ML regression approach predicts resonant frequency, where random forest achieves the highest accuracy (R² = 87.70%).

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

WBAN communication modes and antenna requirements

Wearable antenna challenges (human-body effects and SAR)

Proposed antenna design and performance

Operating bands and measured RF parameters

Radiation pattern characteristics

SAR validation against FCC/ICNIRP limits

Bending robustness tests and regression-based resonant prediction

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications Muhammad S. Yahya1, Umar Musa2, Mohammad S. Zidan3, Socheatra Soeung1, Lila Iznita Izhar1, Z. Zakaria4 & Ahmed J. A. AL-Gburi4􀀍 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. Keywords Machine learning, Wearable antenna, Wireless body area network (WBAN), Dual-band, Regression algorithm, Specific absorption rate (SAR), Bending investigation The field of wearable electronics in wireless WBAN has gained significant interest due to its diverse applications, including fitness tracking, smartwatches, medical care, military systems, and IoT technologies1,2 as illustrated in Fig. 1. WBAN communication operates through three distinct modes on-body, off-body, and in-body – depending on the spatial arrangement of signal nodes3,4. To meet the growing demand for high data rates and reliable connectivity, antennas play a crucial role. However, designing antennas on flexible materials suitable for curved surfaces or direct integration with the human body remains a key challenge for researchers. Wearable antennas offer notable advantages, such as bendability for conformal placement, unobtrusive design, lightweight construction, and low cost, making them ideal for WBAN applications5–7. In practice, these antennas are often embedded into clothing or wearable devices, which operate in close proximity to the body. A critical concern is the degradation of antenna performance caused by the human body’s electromagnetic interaction. Additionally, minimizing radiation exposure to biological tissues is essential for user safety. Organizations such as the Federal 1Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia. 2Department of Electrical Engineering, Bayero University, Kano 3011Kano PMB, Nigeria. 3Department of Electrical Techniques, Technical Institute of Anbar, Middle Technical University, Baghdad 10074, Iraq. 4Center for Telecommunication

FAQs

What problem does the ML-optimized wearable antenna address in WBANs?

It tackles human-body-induced electromagnetic interference, limited bandwidth, and SAR compliance constraints that reduce the performance of conventional wearable antenna designs.

Which frequency bands does the proposed antenna operate at, and what are its key performance results?

It operates 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 radiation efficiencies of 91% and 93%.

How is safety ensured for real wearable use?

Specific Absorption Rate (SAR) analysis verifies compliance, with values below FCC and ICNIRP regulatory thresholds for both 1 g and 10 g tissue averaging.

Aug 5, 2026

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