Autor según el artículo: Al-Molegi, Abdulrahman; Martinez-Balleste, Antoni;
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Martínez Ballesté, Antoni
Palabras clave: Wandering Trajectory analysis Monitoring system Model Mild cognitive impairments Location data Dementia
Resumen: Due to society aging, age-related issues such as mild cognitive impairments (MCI) and dementia are attracting the attention of health professionals, scientists and governments. Seniors suffering from such impairments notice a slight deterioration in their cognitive abilities, which may lead to memory loss and behavioural disorders. In consequence, such seniors refrain from doing their everyday outdoor activities. Technology, e.g. smartphones, wearables and artificial intelligence, can provide seniors and their relatives with a variety of monitoring tools. In a nutshell, locations are analysed and, under specific situations, alarms are raised so that caregivers urgently informed. In this context, the discovery and prediction of trajectories and behaviours play a key role in deploying effective monitoring solutions. In this paper, we present a real-time smartphone-based monitoring system, called SafeMove, to discover and predict elderly people behaviours by analyzing outdoor trajectories. This is achieved by firstly analysing the elder's mobility data previously collected using the proposed model called SpaceTime-Convolutional Neural Network (ST-CNN) in order to predict the most popular locations he/she might visit in the next time. Based on the predicted locations, the elder can be monitored in bounded region. Time and space-related variables, such as the distance traversed, the direction of the movements and the time spent, are analyzed in our abnormal behaviour detection (ABD) model that takes advantage of recurrent neural networks (RNNs). The effectiveness and the efficiency of our system for predicting the next location and detection the abnormal behaviors are evaluated using different datasets comprising real-world GPS trajectories.
Áreas temáticas: Zootecnia / recursos pesqueiros Software Matemática / probabilidade e estatística Interdisciplinar Engenharias iv Engenharias iii Engenharias i Computer science, artificial intelligence Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: antoni.martinez@urv.cat
Identificador del autor: 0000-0002-1787-7410
Fecha de alta del registro: 2024-08-24
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://link.springer.com/article/10.1007/s00521-022-07320-3
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Neural Computing & Applications. 34 (19): 16785-16803
Referencia de l'ítem segons les normes APA: Al-Molegi, Abdulrahman; Martinez-Balleste, Antoni; (2022). SafeMove: monitoring seniors with mild cognitive impairments using deep learning and location prediction. Neural Computing & Applications, 34(19), 16785-16803. DOI: 10.1007/s00521-022-07320-3
DOI del artículo: 10.1007/s00521-022-07320-3
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications