This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10–20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

This paper presents the basic ideas of eye and gaze tracking in multiple-display environment. The algorithm for display detection and identification is described as well as the rules for gaze interaction in multi display environment. The core of the method is to use special LED markers and eye and scene tracking glasses. Scene tracking camera registers markers position which is then represented as a cloud of points. Analyzing the mutual positions of detected points the algorithm estimates screen/displays position. Display number is assigned based on special information marker. Described project shows the possibility of hands free interaction in MDE. It also presents how to register visual attention when information is dispersed on several screens.

In this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal images requires smart algorithms for automatic processing of millions of thermal frames. Instance image segmentation allows to extract each animal from a frame and track its activity and thermal patterns. In this work, we adopted two instance segmentation algorithms, i.e., Mask R-CNN and TensorMask. Both methods in different configurations were applied to a set of thermal sequences, and both achieved high results. The best results were obtained for the TensorMask model, initially pre-trained on visible light images and finally trained on thermal images of rodents. The achieved mean average precision was above 90 percent, which proves that model pre-training on visible images can improve results of thermal image segmentation.

Familial hypercholesterolemia (FH) is a highly undiagnosed disease. Among FH patients, the onset of premature coronary artery disease is 13 times higher than in the general population. Early diagnosis and treatment is essential to prevent cardiovascular diseases and their complications, and to prolong life. One of the clinical criteria of FH is the occurrence of a corneal arcus (CA) among patients, especially those under 45 years old. Therefore, by detecting a CA, it might be possible to reduce the number of undiagnosed FH cases. In this paper, we propose using convolutional neural networks (CNN) for automatic recognition of the presence of a corneal arcus. To achieve this goal, we created a dataset of images of irises containing different stages of CA as well as irises without a CA. The core of the dataset consists of images acquired from patients with a corneal arcus, enroled in the National Centre of Familial Hypercholesterolemia in Gdansk. To increase the number of images, the dataset was complemented with images downloaded from the Internet. This dataset created for training and testing the model consisted of nearly 4000 images. To detect a CA in photographic images, we tested neural network models based on the VGG16, ResNet and Inception architectures. Finally, the performance of the models was evaluated on a set of images acquired from volunteers with a custom mobile application. The accuracy of CA detection in a real life scenario was 88% and the F1 score was 86%

There are many approaches to non-invasively deter-mine blood pressure. Among them there are methods based on utilizing pulse transit time measured by means of photoplethys-mography. The variability of the blood pressure drop between two measurement sensors placed along the artery and its dependence on the selected parameters describing the cardiovascular system is presented in the paper. This pressure drop modifies the pressure pulse propagation velocity, thus also value of PTT. The properties of four fiducial points on the radial and brachial arteries were analyzed. Publicly available simulation data were used for the analyses. The best results were achieved for the point defined by the maximum of the first derivative of the signal.