Articles producció científica> Enginyeria Informàtica i Matemàtiques

SafeMove: monitoring seniors with mild cognitive impairments using deep learning and location prediction

  • Identification data

    Identifier: imarina:9267686
    Authors:
    Al-Molegi, AbdulrahmanMartinez-Balleste, Antoni
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Al-Molegi, Abdulrahman; Martinez-Balleste, Antoni;
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Martínez Ballesté, Antoni
    Keywords: Wandering Trajectory analysis Monitoring system Model Mild cognitive impairments Location data Dementia
    Abstract: 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: antoni.martinez@urv.cat
    Author identifier: 0000-0002-1787-7410
    Record's date: 2024-08-24
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://link.springer.com/article/10.1007/s00521-022-07320-3
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Neural Computing & Applications. 34 (19): 16785-16803
    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
    Article's DOI: 10.1007/s00521-022-07320-3
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Artificial Intelligence,Computer Science, Artificial Intelligence,Software
    Wandering
    Trajectory analysis
    Monitoring system
    Model
    Mild cognitive impairments
    Location data
    Dementia
    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
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