Human Pose Estimation is a computer vision-based technology that identifies and classifies specific points on the human body. These points represent our limbs and joints to calculate the angle of flexion, and estimate, well, human pose. While it sounds awkward, knowing the right angle of a joint in a specific exercise is the basis of work for physiotherapists, fitness trainers, and artists. Implementing such capabilities for a machine results in surprisingly useful applications in different fields. We’ll figure out its principle of work and capabilities to understand suitable business cases. Also, we’ll analyze different approaches to Human Pose Estimation as a machine learning technology, and try to define the applications for each. The process usually involves the extraction of joints on a human body, and then analysis of a human pose by deep learning algorithms. If the human pose estimation system uses video records as a data source, key points (joints locations) are detected from a sequence of frames, not a single picture. It allows us to achieve more accuracy as the system analyzes an actual movement of a person, not a steady position.