Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments For Independent Assistive Living

Umer Saeed, Syed Yaseen Shah, Syed Aziz Shah, Ahmad Jawad, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan, Akram Alomainy , Qammer H. Abbasi

    Research output: Contribution to journalArticlepeer-review

    23 Citations (Scopus)
    77 Downloads (Pure)


    Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.
    Original languageEnglish
    Article number2237
    Number of pages16
    JournalElectronics (Switzerland)
    Issue number18
    Publication statusPublished - 12 Sept 2021

    Bibliographical note

    This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited


    Taif University Research Grant under Project TURSP-2020/277.


    • radio-frequency
    • next generation healthcare
    • contactless monitoring
    • fall detection
    • deep learning
    • ResNet
    • Deep learning
    • Radio-frequency
    • Contactless monitoring
    • Fall detection
    • Next generation healthcare

    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering
    • Control and Systems Engineering
    • Hardware and Architecture
    • Computer Networks and Communications


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