Risk assessment system of fall in the elderly using artificial intelligence and cloud computing
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Blasco García, Jesús Damián; Pavón Pulido, Nieves; López Riquelme, Juan Antonio; Feliu Batlle, Jorge Juan; Nieto Galera, Raúl; [et al.]Área de conocimiento
Ingeniería de Sistemas y AutomáticaPatrocinadores
This work has been partially supported by the project 21668/ PDC/21 Program Proof of Concept included in the “ProgramaSéneca 2021” Region of Murcia, Spain. This article is also the result of the activity carried out in the research groups: NEUROCOR (Neurotechnology, Control and Robotics) at the Technical University of Cartagena, Spain, and NiCE (Clinical and Experimental Neuroscience) at the School of Medicine, Campus Mare Nostrum, University of Murcia, Spain. The Alzheimer and other neurodegenerative dementias association “AFAL Cartagena y Comarca” deserves a special mention in this work for its support in providing contacts for carrying out trials with real patients.Fecha de publicación
2022-08Editorial
Austin Publishing GroupCita bibliográfica
Blasco-García JD, Pavón-Pulido N, López-Riquelme JA, Feliu-Batlle JJ, Nieto-Galera R and Herrero MT. Risk Assessment System of Fall in the Elderly Using Artificial Intelligence and Cloud Computing. Phys Med Rehabil Int. 2022; 9(2): 1204Revisión por pares
siPalabras clave
Artificial intelligenceTinetti scale
Falling risk
Telemedicine
Cloud computing
Resumen
This paper presents a Cloud-based online tool for helping health
professionals to predict the risk of falling in the elderly by using the well-known
Tinetti’s Test. This tool implements a Deep Learning-based method for allowing
several Tinetti scale’s items to be automatically estimated, simply using a
conventional camera or a recorded video. From these sources of information,
patients’ skeleton is recognized and their movements analyzed by applying some
geometric calculations, which provide an objective risk assessment. Results are
represented as a set of plots easily interpretable by experts. Several tests, in
a controlled environment, have been carried out to validate the accuracy and
reliability of the system. Moreover, some tests have been also made with real
elderly patients, whose results have been evaluated by therapists. The benefits
of using such remote tool for assessing (objective) fall risk, from a usability point
of view, are also highlighted.
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