The purpose of this paper is to develop an information system that would detect a fall based on video stream analysis. This work contains research and a description of the subject environment and the structure of the system. An analysis of current scientific works in this area was conducted, which allowed defining the purpose and primary objectives of the thesis. In addition, this analysis showed that this system would be in demand in the market of software that uses computer vision. The practical significance of the results can be determined by the fact that the proposed application performs on the level of other research works in this field and can be used by a broad market of customers. We applied pre-trained TensorFlow Lite CNN real-time model PoseNet to detect keypoints of a subject. Keypoints sequence is analyzed by LSTM as it works best when processing long sequences of data. The system was trained using a combination of the two biggest fall detection datasets. The system extracts the locations of 17 joint points of the human body and recognizes human movement by detecting the joint point location changes. By using joint points location as input to LSTM, the training and inference time was improved drastically. It also helped minimize the adverse effects of light sources and shadows