Privacy-Preserving Wandering Behaviour Sensing in Dementia Patients using Modified Logistic and Dynamic Newton Leipnik Maps

Syed Aziz Shah, Jawad Ahmad, Fawad Masood, Syed Yaseen Shah, Haris Pervaiz , William Taylor, Muhammad Ali Imran , Qammer H. Abbasi

    Research output: Contribution to journalArticlepeer-review

    25 Citations (Scopus)
    43 Downloads (Pure)

    Abstract

    The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the ’unstable incapacity’. This health status is essentially determined by the apparent decline of independence in Activities of Daily Living (ADLs). Detecting ADLs provide possibilities of improving the home life of elderly people as it can be applied to fall detection systems.. This article looks at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this article were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this article is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data.
    Original languageEnglish
    Article number9187640
    Pages (from-to)3669 - 3679
    Number of pages11
    JournalIEEE Sensors Journal
    Volume21
    Issue number3
    Early online date7 Sept 2020
    DOIs
    Publication statusPublished - 1 Feb 2021

    Funder

    Engineering and Physical Sciences Research Council (EPSRC), EP/T021020/1 and EP/T021063/1.

    Funding

    Manuscript received July 24, 2020; accepted September 4, 2020. Date of publication September 7, 2020; date of current version January 6, 2021. This work was supported in parts by Engineering and Physical Sciences Research Council (EPSRC), EP/T021020/1 and EP/T021063/1. The associate editor coordinating the review of this article and approving it for publication was Prof. Chang-Hee Won. (Corresponding author: Syed Aziz Shah.) Syed Aziz Shah is with the Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, U.K. (e-mail: [email protected]). Jawad Ahmad is with the School of Computing, Edinburgh Napier University, Edinburgh EH11 4DY, U.K. Fawad Masood is with the Institute of Space Technology, Islamabad 44000, Pakistan. Syed Yaseen Shah is with the School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, U.K. Haris Pervaiz is with the School of Computing, Lancaster University, Lancaster LA1 4YW, U.K. William Taylor, Muhammad Ali Imran, and Qammer H. Abbasi are with the James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, U.K. Digital Object Identifier 10.1109/JSEN.2020.3022564 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

    FundersFunder number
    Engineering and Physical Sciences Research CouncilEP/T021020/1, EP/T021063/1
    Fundação para a Ciência e a TecnologiaIncentivo/SAU/LA0001/2013

    Keywords

    • Wandering behavior
    • human activity
    • machine learning
    • patient monitoring
    • wireless sensing

    ASJC Scopus subject areas

    • Instrumentation
    • Electrical and Electronic Engineering

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