During the first stage of the project a computer vision system for human motion tracking was developed. For its implementation, a pair of cameras and a bicycle trainer are required. Human movement is monitored in real time by an effective algorithm that determines the key angles between the joints of a person exercising on a bike trainer. The following phase of the work focused on comparing the discussed system with a reference professional set, based on human motion sensors. The gathered data was then further analysed and compared, indicating that the accuracy of the developed system is fully satisfactory.

The aim of this work was to develop a proprietary device for real-time cough detection in audio recordings. Using models such as CNN, ResNet-50, and MobileNet, the system classifies cough sounds, enabling the early detection of potential infection cases, such as COVID-19. The device is intended for use in both private and public spaces, including medical facilities, nursing homes, and doctor's offices.

The aim of the research was to develop a new device for monitoring and diagnosing functional disorders of the lower urinary tract, which will enable measurement of muscle tension (EMG) and electrical impedance tomography (EIT) with the possibility of electrostimulation of bladder reconstruction.