The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented.

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This paper reviews non-invasive blood glucose measurements via dielectric spectroscopy at microwave frequencies presented in the literature. The intent is to clarify the key challenges that must be overcome if this approach is to work, to suggest some possible ways towards addressing these challenges and to contribute towards prevention of unnecessary ‘reinvention of the wheel’.

SAFE (Scan and Find Early) is a novel microwave imaging device intended for breast cancer screening and early detection. SAFE is based on the use of harmless electromagnetic waves and can provide relevant initial diagnostic information without resorting to X-rays. Because of SAFE’s harmless effect on organic tissue, imaging can be performed repeatedly. In addition, the scanning process itself is not painful since breast compression is not required. Because of the absence of physical compression, SAFE can also detect tumors that are close to the thoracic wall. A total number of 115 patients underwent the SAFE scanning procedure, and the resultant images were compared with available magnetic resonance (MR), ultrasound, and mammography images in order to determine the correct detection rate. A sensitivity of 63% was achieved. Breast size influenced overall sensitivity, as sensitivity was lower in smaller breasts (51%) compared to larger ones (74%). Even though this is only a preliminary study, the results show promising concordance with clinical reports, thus encouraging further SAFE clinical studies.

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In the past decade, extensive research on dielectric properties of biological tissues led to characterization of dielectric property discrepancy between the malignant and healthy tissues. Such discrepancy enabled the development of microwave therapeutic and diagnostic technologies. Traditionally, dielectric property measurements of biological tissues is performed with the well-known contact probe (open-ended coaxial probe) technique. However, the technique suffers from limited accuracy and low loss resolution for permittivity and conductivity measurements, respectively. Therefore, despite the inherent dielectric property discrepancy, a rigorous measurement routine with open-ended coaxial probes is required for accurate differentiation of malignant and healthy tissues. In this paper, we propose to eliminate the need for multiple measurements with open-ended coaxial probe for malignant and healthy tissue differentiation by applying support vector machine (SVM) classification algorithm to the dielectric measurement data. To do so, first, in vivo malignant and healthy rat liver tissue dielectric property measurements are collected with open-ended coaxial probe technique between 500 MHz to 6 GHz. Cole-Cole functions are fitted to the measured dielectric properties and measurement data is verified with the literature. Malign tissue classification is realized by applying SVM to the open-ended coaxial probe measurements where as high as 99.2% accuracy (F1 Score) is obtained.

An analytical formulation for relative dielectric constant retrieval is reconstructed to establish a relationship between the response of a spiral microstrip resonator and effective relative dielectric constant of a lossy superstrate, such as biological tissue. To do so, an analytical equation is modified by constructing functions for the two unknowns, the filling factor and effective length of the resonator. This is done by simulating the resonator with digital phantoms of varying permittivity. The values of and are determined for each phantom from the resulting S-parameter response, using particle swarm optimization. Multiple nonlinear regression is applied to produce equations for and , expressed as a function of frequency and the phantom's relative dielectric constant. These equations are combined to form a new nonlinear analytical equation, which is then solved using the Newton-Raphson iterative method, for both simulations and measurements of physical phantoms. To verify the reconstructed dielectric constant, the dielectric properties of the physical phantoms are determined with commercial high temperature open-ended coaxial probe. The dielectric properties are reconstructed by the described method, with less than 3.67% error with respect to the measurements.Index Terms-Dielectric property retrieval, high-loss materials, high-permittivity materials, non-invasive glucose monitoring, spiral resonator. 0018-9480

The proper management of renal lithiasis presents a challenge, with the recurrence rate of the disease being as high as 46%. To prevent recurrence, the first step is the accurate categorization of the discarded renal calculi. Currently, the discarded renal calculi type is determined with the X-ray powder diffraction method which requires a cumbersome sample preparation. This work presents a new approach that can enable fast and accurate classification of discarded renal calculi with minimal sample preparation requirements. To do so, first, the measurements of the dielectric properties of naturally formed renal calculi are collected with the open-ended contact probe technique between 500 MHz to 6 GHz with 100 MHz intervals. Cole-Cole parameters are fitted to the measured dielectric properties with the generalized Newton-Raphson method. The renal calculi types are classified based on their Cole-Cole parameters as calcium oxalate, cystine, or struvite. The classification is performed using nearest neighbors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.Keywords: Dielectric properties of renal calculi, kidney stone, open-ended coaxial probe, Cole-Cole parameters, classification of kidney stones, machine learning, k-nearest neighbors 5 proper medication and dietary restrictions. However, to designate the appropriate prescription, the renal calculi types should be determined by analyzing the discarded material. There are four major renal calculi types: calcium oxalate (CaOx), cystine, struvite, and uric acid. Types of renal calculi can be