The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

We propose a high-performance and robust control of a transformerless, single-phase PV inverter in the standalone mode. First, modeling and design of a DC-DC boost converter using a nonlinear back-stepping control was presented. The proposed converter uses a reference voltage that is generated by the Perturb and Observe (P&O) algorithm in order to extract the maximum power point (MPP) by responding accurately to varying atmospheric conditions. Another goal for using the boost converter is to raise the voltage at the input of the inverter without using a transformer in this system, thus making the system more compact and less expensive. Secondly, the single-phase H-bridge inverter was controlled by using back-stepping control in order to eliminate the error between the output voltage of the inverter and the desired value, even if there is acute load variation at the output of the inverter. The stability of the boost converter and H-bridge inverter was validated by using Lyapunov’s stability theory. Simulation results show that the proposed PV system with back-stepping controllers has a good extraction of the MPP with an efficiency of 99.93% and 1 ms of response time. In addition, the sinusoidal form of the output voltage of the inverter is fixed to 220 V and the total harmonic distortion of the output voltage was found to be less than 1%.

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Our study shows CHO, LIP, and PRO intakes can be easily captured by an application on smartphone for meal entry used by T1D patients. Although LIP and PRO meal contents did not influence glucose levels when insulin doses were based on CHO in this pilot study, this application could be used for further investigation of this topic, including in closed-loop conditions.