Elderly fall detection
The elderly population in Singapore is increasing and is expected to rise to a quarter of the Singapore population by 2030. This has raised an alarming concern within the nation and community as we have to learn and equip ourselves with the necessary knowledge and skills to take care and look out...
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2022
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sg-ntu-dr.10356-1578132023-07-07T19:12:54Z Elderly fall detection Tan, Qi Jian Law Choi Look School of Electrical and Electronic Engineering ECLLAW@ntu.edu.sg Engineering::Electrical and electronic engineering The elderly population in Singapore is increasing and is expected to rise to a quarter of the Singapore population by 2030. This has raised an alarming concern within the nation and community as we have to learn and equip ourselves with the necessary knowledge and skills to take care and look out for this vulnerable age group. On top of this, the percentage of elderly living in isolation and developing sense of depression is also on a rise. This is especially concerning, especially with the current Covid- 19 situation, where due to the rules and regulations, there are fewer physical house visits. Thus, if the elderly encounters a situation such as falling, no one will know. Despite the government efforts to promote various useful application to the public, the elderly population may not be receptive to it due to the lack of knowledge on mobile application. The large influx of information may have an adverse effect on the elderly as they would be more confused, diminishing the intended outcome of these applications. Thus, this mobile application aims to develop a mobile application that include assisted living functions for the elderly, such as directory of hotlines, to-do-list, fall sensor etc. Even the aesthetic part of the mobile application is taken into consideration to fit the elderly needs. Hopefully, this provides the elderlies with the necessary functions to make their day-to-day life easier. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-23T13:31:18Z 2022-05-23T13:31:18Z 2022 Final Year Project (FYP) Tan, Q. J. (2022). Elderly fall detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157813 https://hdl.handle.net/10356/157813 en A3103-211 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Tan, Qi Jian Elderly fall detection |
description |
The elderly population in Singapore is increasing and is expected to rise to a quarter of the
Singapore population by 2030. This has raised an alarming concern within the nation and
community as we have to learn and equip ourselves with the necessary knowledge and skills
to take care and look out for this vulnerable age group.
On top of this, the percentage of elderly living in isolation and developing sense of
depression is also on a rise. This is especially concerning, especially with the current Covid-
19 situation, where due to the rules and regulations, there are fewer physical house visits.
Thus, if the elderly encounters a situation such as falling, no one will know.
Despite the government efforts to promote various useful application to the public, the elderly
population may not be receptive to it due to the lack of knowledge on mobile application. The
large influx of information may have an adverse effect on the elderly as they would be more
confused, diminishing the intended outcome of these applications.
Thus, this mobile application aims to develop a mobile application that include assisted living
functions for the elderly, such as directory of hotlines, to-do-list, fall sensor etc. Even the
aesthetic part of the mobile application is taken into consideration to fit the elderly needs.
Hopefully, this provides the elderlies with the necessary functions to make their day-to-day
life easier. |
author2 |
Law Choi Look |
author_facet |
Law Choi Look Tan, Qi Jian |
format |
Final Year Project |
author |
Tan, Qi Jian |
author_sort |
Tan, Qi Jian |
title |
Elderly fall detection |
title_short |
Elderly fall detection |
title_full |
Elderly fall detection |
title_fullStr |
Elderly fall detection |
title_full_unstemmed |
Elderly fall detection |
title_sort |
elderly fall detection |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/157813 |
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1772827499051352064 |