Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system Article Swipe
Adeel Akram
,
Muhammad Bilal Khan
,
Najah Abed Abu Ali
,
Qixing Zhang
,
Awais Ahmad
,
Muhammad Shahid Iqbal
,
Syed Atif Moqurrab
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.dcan.2024.07.009
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.dcan.2024.07.009
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.
Related Topics
Concepts
Computer science
Robustness (evolution)
Wearable computer
Cognitive radio
Machine learning
Wireless
Artificial intelligence
Software
Adaptability
Flexibility (engineering)
Context (archaeology)
Real-time computing
Embedded system
Telecommunications
Paleontology
Statistics
Programming language
Chemistry
Biology
Mathematics
Biochemistry
Ecology
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.dcan.2024.07.009
- OA Status
- diamond
- Cited By
- 2
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401266744
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401266744Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.dcan.2024.07.009Digital Object Identifier
- Title
-
Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing systemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-02Full publication date if available
- Authors
-
Adeel Akram, Muhammad Bilal Khan, Najah Abed Abu Ali, Qixing Zhang, Awais Ahmad, Muhammad Shahid Iqbal, Syed Atif MoqurrabList of authors in order
- Landing page
-
https://doi.org/10.1016/j.dcan.2024.07.009Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.dcan.2024.07.009Direct OA link when available
- Concepts
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Computer science, Robustness (evolution), Wearable computer, Cognitive radio, Machine learning, Wireless, Artificial intelligence, Software, Adaptability, Flexibility (engineering), Context (archaeology), Real-time computing, Embedded system, Telecommunications, Paleontology, Statistics, Programming language, Chemistry, Biology, Mathematics, Biochemistry, Ecology, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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