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Technological advancements in medical imaging techniques are opening new possibilities for diagnostics and monitoring health conditions. This study aimed to develop and evaluate a portable ultrasound-impedance tomograph designed for long-term monitoring of the lower urinary tract. This device combines ultrasound and impedance technologies, enhancing imaging accuracy and the non-invasiveness of procedures, which is particularly crucial for patients requiring regular examinations. The study used a prototype tomograph on patients with lower urinary tract disorders. To assess the device's effectiveness, images obtained from traditional diagnostic methods were compared with those generated by the new tomograph. This device utilizes advanced ultrasound beamforming technology and multiplexing of measurement channels, allowing for rapid and accurate diagnostics without requiring invasive sensor placement within the patient's body. The findings indicate that the newly developed tomography provides high-resolution and high-contrast images, facilitating better pathology identification than traditional methods. It was also demonstrated that the device effectively monitors changes over time, crucial for planning long-term treatment and monitoring patients. The development of this device opens new possibilities in diagnosing and monitoring medical conditions, enabling more precise and less burdensome diagnostic procedures. Its application could significantly improve the quality of life for patients and the efficacy of therapies in urology and other medical fields.

Human movement analysis is critical to optimizing sports training and influencing exercise intensity and effectiveness. In the age of modern technology, more and more advanced systems are emerging to support coaches and expand the range of analysis performed. This article aims to verify that artificial intelligence, together with machine learning algorithms, can accurately classify exercises in a dynamic gym environment and effectively assess the correctness of their performance. For the initial analysis of movement, the Google MediaPipe Pose model was used, which was responsible for detecting the human silhouette and determining the coordinates of the position of critical joints. Based on these coordinates, the angles between each joint were calculated, and then their sequences were further analyzed. The sequences were analyzed using the following three algorithms: support vector machine (SVM), dense neural network, and LSTM recurrent network. As a result, the system based on recurrent LSTM networks achieved the best prediction efficiency of approximately 98%, enabling accurate exercise classification. Subsequently, verification of the activities' correctness was also carried out, and the system, based on recursive LSTM networks, again achieved the best efficiency, this time equal to 96% on average for all exercises. On this basis, it was concluded that the discussed approach enables practical analysis of human movement, which can significantly improve training methods and facilitate coaching work.