## 5 Conclusions  
This research focused on the development of a system for measuring finger joint angles based on camera image and was intended to work within the field of medicine to track the movement and limits of hand mobility in multiple sclerosis. The convolution neural network for analysis of static RGB images from the camera for 12 finger joint angle measuring was developed accordingly, with the best results being obtained after applying a neural network model based on processing images with subtracted background of the hand. The neural network developed allows non-contact measurement of finger joint angles to be undertaken using a single RGB camera without the use of additional sensors. The use of a single ordinary RGB camera in this research makes the system developed for finger angle measuring cheaper, and easier to install and operate compared to sensor-based systems and specialist cameras. A camera must support at least 30 frames/sec and the resolution 720p for the correct neural network work. A camera with better parameters will give better results. Image analysis approaches based on different neural network architectures were considered to achieve a lightweight and fast neural network for real-time work. The option of building a neural network based on convolutional pose machine architecture was considered. However, convolutional pose machine-based neural network architecture is a slow algorithm and not suitable for real-time operation on low computing power devices. It also does not work correctly for hands with unusual parameters. The convolutional pose machine-based neural network architecture was removed from the final version of the neural network to improve performance. However, the results of convolutional pose machine-based image analysis and markup were used to train a light MobileNetV2 based neural network. The use of MobileNetV2 architecture, originally developed for image neural network processing on mobile devices, in turn makes it possible to run the system on systems with low computing power. The system developed in the article will thus make it possible to take high-precision and high-speed measurements of the finger position at low cost. Accuracy of the neural network developed can, probably, be further improved by analyzing the camera image sequences and neural network hand joint angles measured. Current neural network analysis of each image is undertaken in the video stream independently and does not analyze time sequences. In future research, it is planned that this approach will be used to improve recognition accuracy of the corners of the finger joints. Although the neural network can work without applying the background subtraction algorithm, the best results are nonetheless obtained after doing so. At this point, for the developed neural network to work, the camera must be stationary during measurement. This algorithm will work even if camera will be moved. However, model quality scientifically decreases because image will have artifacts and deformations. This model created for medical aims and must have good quality, but this model cannot give good quality if camera is not static. In the future the plan is to replace the background subtraction algorithm used now with a neural network based on the MaskRCNN algorithm, which will be much less susceptible to loss of accuracy as a result of variations in brightness or bias. For now, the MaskRCNN algorithm is expected to be based on the MobileNetV2 architecture neural network developed in this study with minimal pre-training. To obtain high performance and reduce the volume of the neural network, the optimization method based on the Monte Carlo method will be applied, and the unused weights in the network will be removed without loss of accuracy. The quantization algorithm will also be applied to the neural network in the future, which should also significantly increase the speed of the neural network by transferring the weights of the neural network from the format float32 to the format float16.