Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system^☆

Abstract

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel State Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

Keywords

AI / Elderly falls / Intelligent learning / SDRF / WCSI

CRediT authorship contribution statement

Adeel Akram: Writing - original draft, Software, Methodology, Conceptualization. Muhammad Bilal Khan: Writing - original draft, Software, Resources, Methodology, Data curation, Conceptualization. Najah Abed Abu Ali: Writing - review & editing, Resources, Investigation, Formal analysis, Data curation, Conceptualization. Qixing Zhang: Writing - review & editing, Validation, Supervision, Investigation, Data curation, Conceptualization. Awais Ahmad: Writing - review & editing, Validation, Resources, Investigation, Formal analysis. Muhammad Shahid Iqbal: Visualization, Validation, Investigation, Formal analysis, Data curation, Conceptualization. Syed Atif Moqurrab: Writing - review & editing, Supervision, Project administration, Investigation, Formal analysis, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported in part by the Institute of Advanced Technology, University of Science and Technology of China (USTC) under Grant PF02023001Y, the Zayed Health Center at United Arab Emirates University (UAEU) under Grant G00003476, and COMSATS University Islamabad, Attock Campus.

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