A Fused Learning and Enhancing Method for Accurate and Noninvasive Hydration Status Monitoring With UWB Microwave Based on Phantom
A Fused Learning and Enhancing Method for Accurate and Noninvasive Hydration Status Monitoring With UWB Microwave Based on Phantom
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DOI：
10.1109/tmtt.2023.3248788
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发表时间：
2023-09
影响因子：
4.3
通讯作者：
Lu Peng;Hang Song;X. Xiao;Guancong Liu;Min Lu;Y. Liu;Bo Wei;T. Kikkawa
中科院分区：
文献类型：
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作者：
Lu Peng;Hang Song;X. Xiao;Guancong Liu;Min Lu;Y. Liu;Bo Wei;T. Kikkawa
水化状态是人体健康的重要指标，但对人体水化状态监测的研究相对较少。传统的水化监测方法存在侵入性强、检测耗时长、不能做到真实的实时监测等缺点。近年来，微波水化监测技术引起了广泛的研究兴趣。该方法是无创的，并且基于具有不同水合状态的人体组织的介电性质的变化。在这篇文章中，提出了一种融合的学习和增强方法，以准确地监测水化，通过使用超宽带（UWB）微波。该方法设计了Elman神经网络学习模型和联合增强方法。联合增强算法由k个中心点和最小二乘法组成。为了验证所提出的方法的性能，肌肉幻影开发使用牛血清白蛋白。通过改变浓度，体模可以模拟不同水合状态的肌肉。在评估实验中，S参数和频率数据测量UWB微波。然后，使用所提出的方法评估水化水平的数据。实验结果表明，该方法对肌肉体模浓度估算的相对误差小于3.3%。这些结果证明了所提出的水化监测方法的可行性，并证明它是有前途的监测人体水化状态。
Hydration status is a critical indicator for human health, yet there are relatively few studies on monitoring the hydration status of the human body. Traditional methods for hydration monitoring have shortcomings such as being invasive, time-consuming to detect, and not being real time. Recently, microwave hydration monitoring has attracted research interest widely. This approach is noninvasive and based on the change of dielectric properties of human tissues with different hydration statuses. In this article, a fused learning and enhancing method is proposed to accurately monitor the hydration by using an ultrawideband (UWB) microwave. In the proposed method, an Elman neural network learning model and a joint enhancing method are designed. The joint enhancing algorithm is composed of $k$ -medoids and least squares. To verify the performance of the proposed method, a muscle phantom is developed using bovine serum albumin. By changing the concentration, the phantom can mimic the muscle with different hydration statuses. In the evaluation experiment, the S-parameter and frequency data were measured with UWB microwave. Then, the data were employed for assessing the level of hydration with the proposed method. Experimental results showed that the relative error for estimating the concentration of the muscle phantom is less than 3.3%. These results demonstrated the feasibility of the proposed hydration monitoring method and proved that it is promising for monitoring human hydration status.