School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2021 Oct 21;21(21):6989. doi: 10.3390/s21216989.
Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.
血糖（BG）浓度监测对于控制糖尿病引起的并发症以及对疾病的数字化管理至关重要。目前，血糖仪被广泛用于测量 BG 浓度。考虑到侵入性 BG 浓度测量涉及疼痛、感染风险、费用和不便等挑战，我们提出了一种基于能量代谢守恒的非侵入性 BG 浓度检测方法。在这项研究中，我们设计并制造了一种多传感器集成检测探头，通过 3D 打印技术可佩戴在手腕上。还应用了两种机器学习算法来建立预测 BG 浓度的回归模型。结果表明，反向传播神经网络模型的性能优于多元多项式回归模型，平均绝对相对差异和相关系数分别为 5.453%和 0.936。在这里，大约 98.413%的预测值在 Clarke 误差网格的 A 区内。上述结果证明了我们从人体手腕进行非侵入性葡萄糖浓度检测的方法和设备的潜力。
