In this work, Al₂O₃ + graphene coatings were prepared using the sol–gel method. The aim of the study was preliminary determination of the influence of size and amount of graphene nanoplatelets on morphology, chemical structure, and basic tribological properties of Al₂O₃ + graphene composite coatings. Two types of reduced graphene oxide (rGO) nanoplatelets with different lateral size and thickness were used to prepare the coatings. To characterize them, scanning electron microscope (SEM), glow discharged optical emission spectrometer (GDOES), Fourier-transform infrared (FTIR), reflectance spectrometer, and ball-on-disk tribological tests were used. It was found that the presence of graphene in the Al₂O₃ + graphene coatings did not fundamentally change the chemical transformation of ceramic Al₂O₃ matrix. Morphology examinations of coatings containing larger graphene nanoplatelets revealed a tendency to their parallel arrangement in relation to the coated surface. The tribological properties of Al₂O₃ + graphene coatings turned out to be strongly dependent on the size of graphene nanoplatelets as well as on the heat treatment temperature. The friction coefficient as low as 0.11 and good durability were obtained for the Al₂O₃ + graphene coating with larger nanoplatelets and heat-treated at 500 °C. The results of conducted research indicate the potential use of Al₂O₃ + graphene composite coatings prepared by the sol–gel method as low-friction ceramic coatings.

Gait analysis may serve various purposes related to health care, such as the estimation of elderly people’s risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors’ viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject’s clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.