机器学习优化的双带可穿戴天线,用于生物医学物联网系统的实时远程患者监控
Umar Musa1, Amor Smida2, Muhammad S Yahya3
1Department of Electrical Engineering, Bayero University Kano, Kano, 700006, Nigeria. umusa.ele@buk.edu.ng.
Scientific reports
|August 22, 2025
概括
这项研究引入了使用机器学习 (ML) 优化医疗监测的双频带可穿戴天线. 该天线表现出高效率和安全性,机器学习模型在预测性能方面达到97.79%的准确性,减少了70%的计算时间.
科学领域:
- 电磁和天线设计
- 生物医学工程
- 机器学习应用
背景情况:
- 穿戴式天线对于远程医疗监控系统至关重要.
- 优化天线性能和确保生物医学应用的安全是关键挑战.
- 机器学习为增强天线设计和预测精度提供了潜力.
研究的目的:
- 设计和优化用于医疗监测的双带可穿戴天线.
- 评估天线的性能,辐射特性和特定吸收率 (SAR).
- 应用机器学习来预测天线参数并提高设计效率.
主要方法:
- 在2.4GHz和5.8GHz的罗杰斯基板上制造双频带天线.
- 性能特征包括带宽,增益,辐射模式和使用CST MWS模拟的SAR评估.
- 实施监督回归机器学习模型来预测反射系数 (S11).
主要成果:
- 该天线实现了高辐射效率 (92%和91.7%) 和合适的带宽.
- 对于各种身体位置 (手臂,胸部,膝盖) 的SAR值处于安全范围内.
- 整体回归ML模型实现了97.79%的准确性 (R平方),计算时间显著减少 (70%).
结论:
- 双频带可穿戴天线是可靠和有效的实时医疗监测.
- 该研究验证了ML在加速天线设计和性能预测中的使用.
- 集成系统可将生命体征 (心率,温度) 实时传输到物联网平台.
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