Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia.
Department of Electrical and Computer Engineering, The University of Alabama, Box 870286, Tuscaloosa, AL, 35487, USA.
Sci Rep. 2023 Mar 28;13(1):5070. doi: 10.1038/s41598-023-31860-w.
A novel method for embedded hardware-based parameter estimation of the Cole model of bioimpedance is developed and presented. The model parameters R, R and C are estimated using the derived set of equations based on measured values of real (R) and imaginary part (X) of bioimpedance, as well as the numerical approximation of the first derivative of quotient R/X with respect to angular frequency. The optimal value for parameter α is estimated using a brute force method. The estimation accuracy of the proposed method is very similar with the relevant work from the existing literature. Moreover, performance evaluation was performed using the MATLAB software installed on a laptop, as well as on the three embedded-hardware platforms (Arduino Mega2560, Raspberry Pi Pico and XIAO SAMD21). Obtained results showed that the used platforms can perform reliable bioimpedance processing with the same accuracy, while Raspberry Pi Pico is the fastest solution with the smallest energy consumption.
提出并开发了一种新的基于嵌入式硬件的生物阻抗 Cole 模型参数估计方法。使用基于生物阻抗实部 (R) 和虚部 (X) 的测量值以及商 R/X 相对于角频率的一阶导数的数值逼近得出的一组方程来估计模型参数 R、α 和 C。使用暴力搜索法来估计参数 α 的最优值。该方法的估计精度与现有文献中的相关工作非常相似。此外，还在安装了 MATLAB 软件的笔记本电脑以及三个嵌入式硬件平台（Arduino Mega2560、Raspberry Pi Pico 和 XIAO SAMD21）上进行了性能评估。结果表明，所使用的平台可以以相同的精度进行可靠的生物阻抗处理，而 Raspberry Pi Pico 是速度最快、能耗最小的解决方案。
