SMARTEYE - for human posture and activity monitoring Part 2
The purpose of this study is to design a system that allows monitoring of the elderly within their household at all times. The report consists of three stages for monitoring – Human Detection, Human Posture Recognition, and Human Activity Recognition. Human detection allows the system to detect t...
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sg-ntu-dr.10356-691162023-03-03T20:57:19Z SMARTEYE - for human posture and activity monitoring Part 2 Lim, Jay Boon Kiat Deepu Rajan School of Computer Engineering DRNTU::Engineering The purpose of this study is to design a system that allows monitoring of the elderly within their household at all times. The report consists of three stages for monitoring – Human Detection, Human Posture Recognition, and Human Activity Recognition. Human detection allows the system to detect the exact location of the elderly whereas, posture recognition and activity recognition will determine the current status of the elderly. Two postures – Standing and Lying, are recognized by the system. By using the posture recognition algorithms, the system will classify the ongoing activity based on the sequence of postures detected. The system will be implemented with background subtraction and Histogram of Oriented Gradients. Information retrieved from the subtracted background and the classified posture will be used to classify the activity. After testing the implemented system, a detection accuracy of 90% has been achieved for human detection and an accuracy of 93% has been achieved for standing posture detection. Although, an accuracy of only 28% has been achieved for lying posture detection, the implemented system managed to detect falling activities accurately. Therefore, a conclusion can be drawn such that the current status of the elderly can be monitored successfully using the implemented system. Bachelor of Engineering (Computer Science) 2016-11-07T02:18:36Z 2016-11-07T02:18:36Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/69116 en Nanyang Technological University 67 p. application/pdf |
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DRNTU::Engineering Lim, Jay Boon Kiat SMARTEYE - for human posture and activity monitoring Part 2 |
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The purpose of this study is to design a system that allows monitoring of the elderly
within their household at all times. The report consists of three stages for monitoring
– Human Detection, Human Posture Recognition, and Human Activity Recognition.
Human detection allows the system to detect the exact location of the elderly
whereas, posture recognition and activity recognition will determine the current
status of the elderly. Two postures – Standing and Lying, are recognized by the
system. By using the posture recognition algorithms, the system will classify the ongoing
activity based on the sequence of postures detected.
The system will be implemented with background subtraction and Histogram of
Oriented Gradients. Information retrieved from the subtracted background and the
classified posture will be used to classify the activity.
After testing the implemented system, a detection accuracy of 90% has been
achieved for human detection and an accuracy of 93% has been achieved for
standing posture detection. Although, an accuracy of only 28% has been achieved for
lying posture detection, the implemented system managed to detect falling activities
accurately.
Therefore, a conclusion can be drawn such that the current status of the elderly can
be monitored successfully using the implemented system. |
author2 |
Deepu Rajan |
author_facet |
Deepu Rajan Lim, Jay Boon Kiat |
format |
Final Year Project |
author |
Lim, Jay Boon Kiat |
author_sort |
Lim, Jay Boon Kiat |
title |
SMARTEYE - for human posture and activity monitoring Part 2 |
title_short |
SMARTEYE - for human posture and activity monitoring Part 2 |
title_full |
SMARTEYE - for human posture and activity monitoring Part 2 |
title_fullStr |
SMARTEYE - for human posture and activity monitoring Part 2 |
title_full_unstemmed |
SMARTEYE - for human posture and activity monitoring Part 2 |
title_sort |
smarteye - for human posture and activity monitoring part 2 |
publishDate |
2016 |
url |
http://hdl.handle.net/10356/69116 |
_version_ |
1759857812925579264 |