Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

Syed Aziz Shah, Ahsen Tahir, Julien Le Kernec, Ahmed Zoha, Francesco Fioranelli

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    Abstract

    The health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform assessment of this health status. The core idea is to develop a generalized system where the data obtained for ADL can be portable across different environments and groups of subjects, and critical events such as falls in mature individuals can be detected. In this context, we have conducted comprehensive experimental campaigns at nine different locations including four laboratory environments and five elderly care homes. A total of 99 subjects participated in the experiments where 1453 micro-Doppler signatures were recorded for six activities. Different machine learning, deep learning algorithms and transfer learning technique were used to classify the ADL. The support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) provided adequate classification accuracies for particular scenarios; however, the autoencoder neural network outperformed the mentioned classifiers by providing classification accuracy of ~ 88%. The proposed system for fall detection in elderly people can be deployed in care centers and is application for any indoor settings with various age group of people. For future work, we would focus on monitoring multiple older adults, concurrently in indoor settings using continuous radar sensor data stream which is limitation of the present system.
    Original languageEnglish
    Pages (from-to)7933-7953
    Number of pages21
    JournalNeural Computing and Applications
    Volume34
    Issue number10
    Early online date19 Jan 2022
    DOIs
    Publication statusPublished - May 2022

    Bibliographical note

    Open Access This article is licensed under a Creative Commons
    Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

    Publisher Copyright:
    © 2022, The Author(s).

    Funder

    Funding Information:
    The authors acknowledge the help from all the volunteers who took part to the data collection, financial support from the UK Engineering and Physical Sciences Research Council EPSRC (Grant EP/R041679/1) for this work, and the precious collaborations of NG Homes Glasgow and Age UK West Cumbria.

    Keywords

    • Activity of daily living
    • Data classification
    • Fall detection
    • Radar sensing

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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