Human pose detection is defined as the process of locating the joints of a person or a crowd given an image or video. Currently, pose detection is widely used for the evaluation of athletes, workers, and the monitoring of patients in clinical settings. However, human pose estimation and fall detection are not easy tasks as it requires experts to manually assess the person's position by using specialized equipment such as e-health devices (watches, bands, handles), markers and high-cost cameras to monitor a limited scenario. The main goal of this article is to implement a marker-less low-cost computer vision system to get the automatic estimation of poses and falls detection recorded on video by calculating the person's joint angle with a high level of adaptability to any space. This proposed model is the first step in the construction of a system that allows monitoring and generating alerts to prevent falls at home and clinical settings.