The monitoring of people in health centers and geriatric homes is performed by rehabilitation professionals who manually evaluate the surveillance cameras for identifying one person’s position, and his physical condition. However, this task is tedious and demands the full attention of the rehabilitation staff because patients with neurological conditions need special care or in some cases the 24/7 monitoring. On the other hand, the use of artificial intelligence in the detection of objects and people through images or videos has presented a great performance. This article presents a methodology based on deep learning for the detection and monitoring of people in closed and open environments using video. The proposed method is non-invasive, low-cost, and evaluates the physical activity and inactivity of people in real-time. Preliminary results in public databases present outstanding results in the monitoring and estimation of caloric expenditure in people in indoor and outdoor spaces.
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.
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