A glucose concentration sensor for microliter-volume water-glucose solutions is presented. The proposed sensor is composed of an open-loop microstrip resonator with a dielectric liquid holder (5-25-μL volume) glued onto the gap between the line ends. The resonator is coupled to two microstrip lines forming a two-port network whose S-parameter response provide information about the dielectric properties of the liquid under study. Three versions of the sensor at resonant frequencies between 2 and 7 GHz are presented. The sensors are assessed by measuring the complex permittivity of standard liquids reported in the scientific literature. Models of the sensors are presented, which properly match the experimental results. This paper presents an experimental study of the sensors as glucose concentration retrievers. The main novelty is the use of Q factor and the maximum S21 of the resonance as sensing magnitudes. The dependence of these parameters on the glucose concentration of the solutions obeys almost linear relationships. Good sensitivities have been obtained for the proposed sensors, which are coherent with previous works by other authors.

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.

A low-cost, additively manufactured, biocompatible glucose sensor based on the changes in the unloaded quality factor ( $Q_{\mathbf {u}}$ ) with a single split-ring resonator is presented. An exposition of the fundamentals for the use of the $Q_{\mathbf {u}}$ as sensing parameter with microwave planar resonant sensors is shown. The convenience of this sensing parameter is analyzed from the theoretical point of view, and practical design and optimization guidelines are inferred with a special focus on the optimization of $Q_{\mathbf {u}}$ sensitivity to glucose concentration. For practical demonstration and experimental assessment, a novel inverted microstrip configuration is considered, built upon a customized structure made with a certified biocompatible material thanks to 3-D printing techniques, which is aimed to provide for a stronger interaction between the electromagnetic fields and the sample. Two metallization solutions are investigated, yielding devices operating at 4.50 and 4.62 GHz, with operating $Q_{\mathbf {u}}$ of 16.36 and 22.00, relative $Q_{\mathbf {u}}$ sensitivities of 1.377 and 2.727, $Q_{\mathbf {u}}$ sensitivities to glucose content within the physiological range of $0.3\times 10^{-3}$ per mg/dL and $0.6\times 10^{-3}$ per mg/dL, sensing areas of approximately $11.7\times8.8$ mm ² and total structure sizes of 950.0 $\times 35.0\times3.5$ mm ³ . The devices show good performance with water–glucose solutions covering a wide range of concentrations, involving physiological as well as industry-related ones.

Post-stroke neurorehabilitation based on virtual therapies are performed completing repetitive exercises shown in visual electronic devices, whose content represents imaginary or daily life tasks. Currently, there are two ways of visualization of these task. 3D virtual environments are used to get a three dimensional space that represents the real world with a high level of detail, whose realism is determinated by the resolucion and fidelity of the objects of the task. Furthermore, 2D virtual environments are used to represent the tasks with a low degree of realism using techniques of bidimensional graphics. However, the type of visualization can influence the quality of perception of the task, affecting the patient's sensorimotor performance. The purpose of this paper was to evaluate if there were differences in patterns of kinematic movements when post-stroke patients performed a reach task viewing a virtual therapeutic game with two different type of visualization of virtual environment: 2D and 3D. Nine post-stroke patients have participated in the study receiving a virtual therapy assisted by PUPArm rehabilitation robot. Horizontal movements of the upper limb were performed to complete the aim of the tasks, which consist in reaching peripheral or perspective targets depending on the virtual environment shown. Various parameter types such as the maximum speed, reaction time, path length or initial movement are analyzed from the data acquired objectively by the robotic device to evaluate the influence of the task visualization. At the end of the study, a usability survey was provided to each patient to analysis his/her satisfaction level. For all patients, the movement trajectories were enhanced when they completed the therapy. This fact suggests that patient's motor recovery was increased. Despite of the similarity in majority of the kinematic parameters, differences in reaction time and path length were higher using the 3D task. Regarding the success rates were very similar. In conclusion, the using of 2D environments in virtual therapy may be a more appropriate and comfortable way to perform tasks for upper limb rehabilitation of post-stroke patients, in terms of accuracy in order to effectuate optimal kinematic trajectories.