Rasool Khan Umhara, Sheikh Javaid A, Junaid Aqib, Ashraf Shazia, Balkhi Altaf A
Department of Electronics & IT, University of Kashmir, Srinagar, Jammu & Kashmir, India.
School of Management, State University of New York at Buffalo, Buffalo, New York, United States of America.
PLoS One. 2025 Feb 3;20(2):e0305203. doi: 10.1371/journal.pone.0305203. eCollection 2025.
With bio-medical wearables becoming an essential part of Internet of Medical things (IoMT) for monitoring the health of workers, patients and others in different environments, antenna play a pivotal role in such wearables. In this communication, a novel Horse shoe shaped antenna (HSPA) meant for such wearables is presented. The vitals of the workers, patients etc. are collected and sent to the IoMT platform for ensuring their safety and monitoring their physical wellbeing. In this article, regression-based Machine learning (ML) techniques are used to facilitate the design of Horse shoe shaped patch antenna to predict the frequency of operation, radiation efficiency and Specific Absorption Rate (SAR) values to accelerate its design process for on-body applications. The HSPA designed resonates at 2.45 GHz in the frequency band of 1.75-2.98 GHz with SAR of 1.89 W/kg for an input power of 16.98 dBm, peak gain of 1.91 dBi and radiation efficiency of 62.07% when mounted on the human body. 1080 samples of data comprising of three EM parameters have been generated using a conventional EM tool by varying the physical and electrical parameters of the design. A detailed comparison of the five regression-based ML algorithms is presented, and it is observed that the ML models help in efficient use of resources while designing an antenna for bio-medical applications.
随着生物医学可穿戴设备成为医疗物联网（IoMT）的重要组成部分，用于监测不同环境下工人、患者和其他人的健康状况，天线在这类可穿戴设备中起着关键作用。在本通信中，提出了一种适用于此类可穿戴设备的新型马蹄形天线（HSPA）。收集工人、患者等的生命体征并发送到IoMT平台，以确保他们的安全并监测他们的身体健康。在本文中，基于回归的机器学习（ML）技术被用于辅助马蹄形贴片天线的设计，以预测其工作频率、辐射效率和比吸收率（SAR）值，从而加速其在人体应用中的设计过程。所设计的HSPA在1.75 - 2.98 GHz频段内于2.45 GHz处谐振，当安装在人体上时，对于16.98 dBm的输入功率，SAR为1.89 W/kg，峰值增益为1.91 dBi，辐射效率为62.07%。通过改变设计的物理和电气参数，使用传统电磁工具生成了包含三个电磁参数的1080个数据样本。给出了五种基于回归的ML算法的详细比较，并且观察到ML模型有助于在设计用于生物医学应用的天线时有效利用资源。
