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 bandwi...
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| Format: | Article |
| Language: | en |
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Springer Nature
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29366/2/02702151220251050442698.pdf http://eprints.utem.edu.my/id/eprint/29366/ https://www.nature.com/articles/s41598-025-27227-y |
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| author | Al Gburi, Ahmed Jamal Abdullah Yahya, Muhammad Sani Soeung, Socheatra Izhar, Lila Iznita Musa, Umar Zidan, Mohammad S. Zakaria, Zahriladha |
| author_facet | Al Gburi, Ahmed Jamal Abdullah Yahya, Muhammad Sani Soeung, Socheatra Izhar, Lila Iznita Musa, Umar Zidan, Mohammad S. Zakaria, Zahriladha |
| author_sort | Al Gburi, Ahmed Jamal Abdullah |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.utem.eprints-29366 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | Springer Nature |
| record_format | eprints |
| spelling | my.utem.eprints-293662025-12-30T07:26:29Z http://eprints.utem.edu.my/id/eprint/29366/ Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications Al Gburi, Ahmed Jamal Abdullah Yahya, Muhammad Sani Soeung, Socheatra Izhar, Lila Iznita Musa, Umar Zidan, Mohammad S. Zakaria, Zahriladha 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. Springer Nature 2025 Article PeerReviewed text en cc_by_4 http://eprints.utem.edu.my/id/eprint/29366/2/02702151220251050442698.pdf Al Gburi, Ahmed Jamal Abdullah and Yahya, Muhammad Sani and Soeung, Socheatra and Izhar, Lila Iznita and Musa, Umar and Zidan, Mohammad S. and Zakaria, Zahriladha (2025) Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications. Scientific Reports, 15. pp. 1-24. ISSN 2045-2322 https://www.nature.com/articles/s41598-025-27227-y 10.1038/s41598-025-27227-y |
| spellingShingle | Al Gburi, Ahmed Jamal Abdullah Yahya, Muhammad Sani Soeung, Socheatra Izhar, Lila Iznita Musa, Umar Zidan, Mohammad S. Zakaria, Zahriladha Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications |
| title | Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications |
| title_full | Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications |
| title_fullStr | Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications |
| title_full_unstemmed | Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications |
| title_short | Machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient WBAN applications |
| title_sort | machine learning-optimized compact dual-band medical syringe-inspired wearable antenna for efficient wban applications |
| url | http://eprints.utem.edu.my/id/eprint/29366/2/02702151220251050442698.pdf http://eprints.utem.edu.my/id/eprint/29366/ https://www.nature.com/articles/s41598-025-27227-y |
| url_provider | http://eprints.utem.edu.my/ |