The work has been aimed to create an overview of available and used methods and ways to determine the concentration of glucose in body fluids, especially from a technical point of view. It also provides an overview of the clinical features of these methods. The survey found that today's market offers a large number of options and approaches to the issue. There are accurate reference laboratory methods, self-monitoring methods for measuring glucose levels using glucometers, or continuous methods for daily monitoring of blood glucose trends and for insulin pump control. However, it must not be forgotten that the development of full closure of feedback is still not complete today. Individual methods cannot always be compared with each other, precisely because of the focus and the use of these methods. Choosing the right method of blood glucose levels in the body measuring can help patients to manage their diabetes mellitus. The methods listed in the overview are divided in terms of measurement continuity and further according to the invasiveness of the method. Finally, the issues of accuracy in the detection of glycaemia variability and the possibility of further development of these methods are discussed, as it is clear from the survey that the development is focused mainly on continuous methods improving that get to the forefront and also on developing a biosensor that is purely non-invasive and continuous.

Accuracy of systems able to recognize in real time daily living activities heavily depends on the processing step for signal segmentation. So far, windowing approaches are used to segment data and the window size is usually chosen based on previous studies. However, literature is vague on the investigation of its effect on the obtained activity recognition accuracy, if both short and long duration activities are considered. In this work, we present the impact of window size on the recognition of daily living activities, where transitions between different activities are also taken into account. The study was conducted on nine participants who wore a tri-axial accelerometer on their waist and performed some short (sitting, standing, and transitions between activities) and long (walking, stair descending and stair ascending) duration activities. Five different classifiers were tested, and among the different window sizes, it was found that 1.5 s window size represents the best trade-off in recognition among activities, with an obtained accuracy well above 90%. Differences in recognition accuracy for each activity highlight the utility of developing adaptive segmentation criteria, based on the duration of the activities.

In this paper, two different piezoelectric transducers—a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)—were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities—walking, walking up and down stairs, jogging and running—were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW–74 µW using both transducers for each joint.

This work analyzes the results of measurements on thermal energy harvesting through a wearable Thermo-electric Generator (TEG) placed on the arms and legs. Four large skin areas were chosen as locations for the placement of the TEGs. In order to place the generator on the body, a special manufactured band guaranteed the proper contact between the skin and TEG. Preliminary measurements were performed to find out the value of the resistor load which maximizes the power output. Then, an experimental investigation was conducted for the measurement of harvested energy while users were performing daily activities, such as sitting, walking, jogging, and riding a bike. The generated power values were in the range from 5 to 50 μW. Moreover, a preliminary hypothesis based on the obtained results indicates the possibility to use TEGs on leg for the recognition of locomotion activities. It is due to the rather high and different biomechanical work, produced by the gastrocnemius muscle, while the user is walking rather than jogging or riding a bike. This result reflects a difference between temperatures associated with the performance of different activities.