Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly

Abstract

Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods.

Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center

“…This has caused security events such as equipment shutdown and manipulation of communication links, among others. The purpose of the educational data center is for students to face a natural environment and be able to manipulate the equipment under the supervision of a teacher; however, since it does not have a control system, this has become a critical point for the institution (Singh and Prasad, 2018 ; Villegas-Ch et al, 2022 ).…”