Human pose estimation is defined as the process of locating joints of a person or a crowd given an image or video. Currently, this estimation is widely used for the evaluation of athletes, workers, and the monitoring of patients in clinical settings. However, human pose estimation is not an easy task 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 evaluate a marker-less low-cost computer vision system to get the automatic estimation of poses and fall detection on video by calculating the person’s joint angle with a high level of adaptability to any space. The proposed model is the first step in the construction of a tool that allows monitoring and generating alerts to prevent falls at home and clinical settings.
