| dc.description.abstract | Recent research indicates that the number of accidents with children has grown every year and their families feel responsible for maintaining the safety of their children full time, generating frustration and guilt when an accident happens. The home environment is one of the main sources of concern for these caregivers when dealing with accidents with young children. The differential of this work is that the Hathor model performs ubiquitous monitoring of children in the home environment for their families, in addition to detecting and avoiding accident risks based on historical contexts. This facilitates the control and monitoring of children by parents. The model has an application with the objective of capturing data from the routine of eating, sleeping, bathroom and activities and notifying parents about risks or an unbalanced routine. The implemented prototype consists of a neural network for the identification of children in real-time images using the YOLO version 5 network and a risk identification module for the detection of the child’s proximity to a predefined risk area, as well as the prediction of the child’s encounter with risk based on their speed of movement through the images. As contributions this work brings a framework for integration between triggers and monitoring systems, in order to supervise a child in the home environment, predicting and reacting to risks identified by the system. A systematic mapping of the area of assistive robotics and its integration with intelligent environments, categorizing the studies found through proposed taxonomies regarding its proposed use, its integration technologies with intelligent environments, its technologies of interaction with human beings, as well as its target Audience. This mapping shows the trend of technologies used in the area, where studies can be found and the growth of the study area in recent years. | |
