Signature Inspired Home Environments Monitoring System Using IR-UWB Technology Article Swipe
Soumya Prakash Rana
,
Maitreyee Dey
,
Mohammad Ghavami
,
Sandra Dudley
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19020385
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/s19020385
Home monitoring and remote care systems aim to ultimately provide independent living care scenarios through non-intrusive, privacy-protecting means. Their main aim is to provide care through appreciating normal habits, remotely recognizing changes and acting upon those changes either through informing the person themselves, care providers, family members, medical practitioners, or emergency services, depending on need. Care giving can be required at any age, encompassing young to the globally growing aging population. A non-wearable and unobtrusive architecture has been developed and tested here to provide a fruitful health and wellbeing-monitoring framework without interfering in a user’s regular daily habits and maintaining privacy. This work focuses on tracking locations in an unobtrusive way, recognizing daily activities, which are part of maintaining a healthy/regular lifestyle. This study shows an intelligent and locally based edge care system (ECS) solution to identify the location of an occupant’s movement from daily activities using impulse radio-ultra wide band (IR-UWB) radar. A new method is proposed calculating the azimuth angle of a movement from the received pulse and employing radar principles to determine the range of that movement. Moreover, short-term fourier transform (STFT) has been performed to determine the frequency distribution of the occupant’s action. Therefore, STFT, azimuth angle, and range calculation together provide the information to understand how occupants engage with their environment. An experiment has been carried out for an occupant at different times of the day during daily household activities and recorded with time and room position. Subsequently, these time-frequency outcomes, along with the range and azimuth information, have been employed to train a support vector machine (SVM) learning algorithm for recognizing indoor locations when the person is moving around the house, where little or no movement indicates the occurrence of abnormalities. The implemented framework is connected with a cloud server architecture, which enables to act against any abnormality remotely. The proposed methodology shows very promising results through statistical validation and achieved over 90% testing accuracy in a real-time scenario.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s19020385
- https://www.mdpi.com/1424-8220/19/2/385/pdf?version=1547805111
- OA Status
- gold
- Cited By
- 24
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2911112015
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2911112015Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s19020385Digital Object Identifier
- Title
-
Signature Inspired Home Environments Monitoring System Using IR-UWB TechnologyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-18Full publication date if available
- Authors
-
Soumya Prakash Rana, Maitreyee Dey, Mohammad Ghavami, Sandra DudleyList of authors in order
- Landing page
-
https://doi.org/10.3390/s19020385Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/19/2/385/pdf?version=1547805111Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/19/2/385/pdf?version=1547805111Direct OA link when available
- Concepts
-
Signature (topology), Computer science, Real-time computing, Embedded system, Engineering, Remote sensing, Geography, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 4, 2022: 5, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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