A noninvasive blood glucose measurement method based on ultrawideband (UWB) microwave detecting technique is proposed for monitoring the patients' blood glucose level. With this method, a noninvasive measurement for the blood glucose determination can be realized by analyzing the received UWB microwave signals. This method is very convenient and is harmless to the patients. The time-frequency features are used to analyze the received signals. The correctness of the method is testified by successfully obtaining the regularity of the energy of the received signals with the range of blood glucose concentration 0-4000 mg/dl in the dispersion case of the configuration. The reliability of the method is also confirmed by obtaining the regularity of the energy of experimental signals at 6.5 GHz for the realistic earlobe phantom with the step of 50 mg/dl in the range of human blood glucose concentration 0-400 mg/dl. The results verify the sensitivity and efficiency of this method.

Ultrawideband (UWB) microwave detection technology, which is low cost and harmless, has been intensively studied and developed for breast cancer detection. In this study, a composite end-to-end framework that consists of convolutional neural network (CNN) and long-short-term memory (LSTM) is proposed, which can realize the tasks of detecting and quadrant locating the breast tumor simultaneously without any complicated microwave imaging processing. In order to verify the proposed network, three datasets are constructed. First, the microwave signals are solved by the auxiliary differential equation-finite-difference time-domain (ADE-FDTD) solver and the magnetic resonance imaging (MRI)-derived breast models with varied breast densities. Then, Simulation datasets 1 and 2 with varied dielectric properties are constructed. Furthermore, an experimental dataset is constructed through an experimental system consisting of the UWB pulse generation module, UWB antennas, the realistic breast phantom, and sampling oscilloscope. With the proposed network, the overall prediction accuracies of the three datasets reach 99.56%, 98.94%, and 89.50%. These promising results demonstrate the effectiveness and accuracy of the proposed deep learning framework for microwave breast cancer detection.

WiFi signal has become accessible everywhere, providing high-speed data transmission experience. Besides the communication service, channel state information (CSI) of the WiFi signals is widely employed for numerous Internet of Things (IoT) applications. Recently, most of these applications are based on analysis of the microwave reflections caused by physical movement of the objective. In this paper, a novel contactless wireless sensing technique named WiEps is developed to measure the dielectric properties of the material, exploiting the transmission characteristics of the WiFi signals. In WiEps, the material under test is placed between the transmitter antenna and receiver antenna. A theoretical model is proposed to quantitatively describe the relationship between CSI data and dielectric properties of the material. During the experiment, the phase and amplitude of the transmitted WiFi signals are extracted from the measured CSI data. The parameters of the theoretical model are calculated using measured data from the known materials. Then, WiEps is utilized to estimate the dielectric properties of unknown materials. The proposed technique is first applied to the ethanol/water mixtures. Then, additional liquids are measured for further verification. The estimated permittivities and conductivities show good agreement with the actual values, with the average error of 4.0% and 8.9%, respectively, indicating the efficacy of WiEps. By measuring the dielectric property, this technique is promising to be applied to new IoT applications using ubiquitous WiFi signals, such as food engineering, material manufacturing process monitoring, and security check.

Non-invasive, painless, and continuous monitoring sensing systems are needed to enhance the life quality of the diabetic. In this paper, two finger-sized ultra-wideband (UWB) antennas are used as detectors of blood glucose level (BGL) sensing system. A dual-band fusion strategy and an improved long-short-term memory (LSTM-R) are introduced based on UWB microwave technique, to overcome the negative effects of antenna miniaturization. The proposed composite end-to-end framework can achieve the high accuracy and robustness of BGL prediction. The safety and reliability of proposed microwave-based sensor were verified by simulation and experiment. The reported sensing system is validated on the medium under tests (MUT) with different glucose solutions and the human trials. For the measurement of MUT, the testing results achieve a root mean squared error (RMSE) of 4.2367 mg/dL and a mean absolute relative difference (MARD) of 2.79%. During human trials, the non-invasive estimation results obtain a low MARD of 5.9%, and are fallen 100% into the clinically acceptable zones by Clark error grid analysis.

As the increasing number of diabetic around the world and the deficiencies of invasive blood glucose detection, such as prickly pain, risk of infection, and no continuous monitoring, a proper noninvasive blood glucose monitoring method is highly desired to deal better with it. In this article, an ultrawide band microwave based technique combined with a cascaded general regression neural network (C-GRNN) is proposed for blood glucose level estimation to achieve noninvasive and intelligent monitoring. Different glucose solutions are tested by using the reported monitoring system in which C-GRNN is used for intelligent detection and frequency choice. The proposed C-GRNN can reduce the prediction interval from 12.3858 to 2.1375 mg/dL compared with the conventional GRNN, and the root-mean-square error (RMSE) reaches 2.20 mg/dL. These results show good estimation performance. We also conduct a continuous-time blood glucose level monitoring on volunteer using the proposed noninvasive method. The final results show well accuracy in which the RMSE and mean absolute percentage error are 13.3208 mg/dL and 9.74%, respectively.