Mikhail Krasnyanskiy

This study is motivated by the severe tribological regime of PA6 composites in VR platforms operating under dry or boundary lubrication, where alternating shear during foot rotation, localised contact pressures, and third-body abrasion concurrently challenge wear resistance and retention of strength. This paper presents the results of research into the properties of composites based on polyamide PA6 and molybdenum disulphide, obtained by combining the components through high-intensity mechanochemical activation in a planetary mill and classical mixing in a turbulence mixer. We demonstrate that varying the energy of the premixing stage (mechanochemical activation versus low-energy premixing) serves as an effective means of interfacial engineering in PA6/MoS₂ composites, enabling simultaneous enhancement of mechanical and tribological properties at low filler contents. Analysis of experimental composite samples using Fourier-transform infrared spectroscopy (FTIR) indicates the interaction between MoS₂ and oxygen-containing groups of polyamide while maintaining its overall chemical composition. According to the TG-DSC curves, modification of polyamide leads to an increase in the melting temperature by 2 °C, while mechanical activation ensures stronger interaction between the matrix and the filler. Compared to pure PA6, the tensile strength of composites increases by 10–20% for mechanoactivated materials and by 5–10% for materials obtained by conventional methods. The mechanical activation effect is observed even at minimal amounts (0.25 and 0.5%) of MoS₂ in composites. The toughness of all composites, regardless of the mixing method, increases by 5–7% compared to pure polyamide. All composites show a 10–20% reduction in the coefficient of friction on steel. Simultaneously, the water absorption of composites becomes 5–20% higher than that of the original material, which indicates a change in structure and an increase in porosity. The obtained composite materials are planned to be used for manufacturing platforms for the movement of virtual reality (VR) operators.

An important aspect of medical rehabilitation using exoskeletons is objective monitoring of the effectiveness of the exercise program. This control is most often manual and relies on the attention of a rehabilitation physician, but advanced rehabilitation systems also use computer vision technology. Topological skeletons generalize large areas of digital images, representing a virtual internal framework of the analyzed object. The patient and the exoskeleton are described either as a set of spatially disparate (but not explicitly related to either the patient or the exoskeleton) topological skeletons, or as branches of a single topological skeleton which does not allow for objective monitoring of joint displacements. A method to solve this problem for medical rehabilitation using an upper-limb exoskeleton is proposed. It includes the following stages: (I) identifying the exoskeleton, as well as upper and lower parts of the patient’s body; (II) independent construction of three topological skeletons (separately for the exoskeleton and for the upper and lower parts of the patient’s body); (III) their integration. This approach allows for accurate, real-time analysis of movements in the upper-limb joints and prompt notification to the rehabilitation physician of any significant deviations in the technique of performing prescribed exercises.

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is proposed, which provides highly accurate detection of users’ movements. Signal preprocessing (noise filtering, segmentation, normalisation) and feature extraction were performed to generate input data for regression and classification models. Various machine learning algorithms are used to recognise motor activity, ranging from classical algorithms (logistic regression, k-nearest neighbors, decision trees) and ensemble methods (random forest, AdaBoost, eXtreme Gradient Boosting, stacking, voting) to deep neural networks, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformers. The algorithm for integrating machine learning models into the exoskeleton control system is considered. In experiments aimed at abandoning proprietary tracking systems (VR trackers), absolute position regression was performed using data from IMU sensors with 14 regression algorithms: The random forest ensemble provided the best accuracy (mean absolute error = 0.0022 metres). The task of classifying activity categories out of nine types is considered below. Ablation analysis showed that IMU and VR trackers produce a sufficient informative minimum, while adding EMG also introduces noise, which degrades the performance of simpler models but is successfully compensated for by deep networks. In the classification task using all signals, the maximum result (99.2%) was obtained on Transformer; the fully connected neural network generated slightly worse results (98.4%). When using only IMU data, fully connected neural network, Transformer, and CNN–GRU networks provide 100% accuracy. Experimental results confirm the effectiveness of the proposed architectures for motor activity classification, as well as the use of a multi-sensor approach that allows one to compensate for the limitations of individual types of sensors. The obtained results make it possible to continue research in this direction towards the creation of control systems for upper exoskeletons, including those used in rehabilitation and virtual simulation systems.

In the organizing of professional training, the assessment of the trainee’s reaction and state in stressful situations is of great importance. Phobic reactions are a specific type of stress reaction that, however, is rarely taken into account when developing virtual simulators, and are a risk factor in the workplace. A method for evaluating the impact of various phobic stimuli on the quality of training is considered, which takes into account the time, accuracy, and speed of performing professional tasks, as well as the characteristics of electroencephalograms (the amplitude, power, coherence, Hurst exponent, and degree of interhemispheric asymmetry). To evaluate the impact of phobias during experimental research, participants in the experimental group performed exercises in different environments: under normal conditions and under the influence of acrophobic and arachnophobic stimuli. The participants were divided into subgroups using clustering algorithms and an expert neurologist. After that, a comparison of the subgroup metrics was carried out. The research conducted makes it possible to partially confirm our hypotheses about the negative impact of phobic effects on some participants in the experimental group. The relationship between the reaction to a phobia and the characteristics of brain activity was revealed, and the characteristics of the electroencephalogram signal were considered as the metrics for detecting a phobic reaction.

Within the scope of this article, the problem of the formalization of physical processes in adaptive training complexes is considered on the example of virtual objects burning. Despite a fairly complete study of this process, the existing mathematical models are not adapted for the application in training complexes, which leads to a significant increase in costs and lower productivity due to the complexity of the calculations. Therefore, an adapted mathematical model is proposed that allows us to formalize the structure of virtual objects of burning, their basic properties and the processes of changing states, starting from the flame development of an object and ending with their complete destruction or extinguishment. The article proposes the use of threshold value diagrams and rules for changing the states of virtual reality objects to solve the problem of the formalization of burning processes. This tool is quite multi-purpose, which allows you to describe various physical processes, such as smoke, flooding, the spread of toxic gases, etc. The area of the proposed formalization approach includes the design and implementation of physical processes in simulators and multimedia complexes using virtual and augmented reality. Thus, the presented scientific research can be used to formalize the physical processes in adaptive training complexes for professional ergatic systems.