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Monitoring discrete activities of daily living of young & older adults using 5.8 GHz frequency modulated continuous wave radar and ResNet algorithm

  • Umer Saeed
    ,
  • Fehaid Alqahtani
    ,
  • Fatmah Baothman
    ,
  • Syed Yaseen Shah
    ,
  • Syed Ikram Shah
    ,
  • Syed Salman Badshah
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well

Abstract

With numerous applications in distinct domains, especially healthcare, human activity detection is of utmost significance. The objective of this study is to monitor activities of daily living using the publicly available dataset recorded in nine different geometrical locations for ninety-nine volunteers including young and older adults (65+) using 5.8 GHz Frequency Modulated Continuous Wave (FMCW) radar. In this work, we experimented with discrete human activities, for instance, walking, sitting, standing, bending, and drinking, recorded for 10 s and 5 s. To detect the list of activities mentioned above, we obtained the Micro-Doppler signatures through Short-time Fourier transform using MATLAB tool and procured the spectrograms as images. The acquired data of the spectrograms are trained, validated, and tested exploiting a state-of-the-art deep learning approach known as Residual Neural Network (ResNet). Moreover, the confusion matrix, model loss, and classification accuracy are used as performance evaluation metrics for the trained ResNet model. The unique skip connection technique of ResNet minimises the overfitting and underfitting issue, consequently resulting accuracy rate up to 91% .

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 28-38 (11 pages)

Publication milestones

  • Published - 11/02/2022

Publication status

Published - 11/02/2022

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

Publication series

  • Publication series name: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
    ISSN (Print): 1867-8211
    ISSN (Electronic): 1867-822X
    Volume: 420 LNICST

ISBN (Print)

9783030955922

ISBN (Electronic)

9783030955939

External Publication IDs

  • ORCID: /0000-0001-7666-838X/work/109434675
  • Scopus: 85125249417

Host publication title

Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings

Host publication editors

  • Masood Ur Rehman
  • Ahmed Zoha

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