Como parte de la colaboración entre el departamento IEECTQAI y el grupo EduQTech, de la Universidad de Zaragoza, que vienen desarrollando en el ámbito de la detección de caídas desde hace más de una década, se ha publicado en abierto el artículo «Comprehensive Review of Vision-Based Fall Detection Systems» en la revista Sensors (indexada en JCR en la categoría Q1) escrito por el doctorando Jesús Gutiérrez, y sus directores Sergio Martín (UNED) y Víctor Rodríguez (EduQTech).
Aquí podéis ver el abstract del artículo:
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers.
Podéis visualizar y descargar el artículo completo, al estar publicado con acceso abierto, en el siguiente enlace: https://doi.org/10.3390/s21030947
Aquí os dejamos la referencia por si queréis citarla:
Gutiérrez, J.; Rodríguez, V.; Martin, S. Comprehensive Review of Vision-Based Fall Detection Systems. Sensors 2021, 21, 947.
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