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Development of fall detection and activity recognition using threshold based method and neural network

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of lif...

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Main Authors: Sai, Siong Jun, Harun @ Ramli, Hafiz Rashidi, Che Soh, Azura, Kamsani, Noor Ain, Raja Ahmad, Raja Mohd Kamil, Ahmad, Siti Anom, Ishak, Asnor Juraiza
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2020
Online Access:http://psasir.upm.edu.my/id/eprint/88434/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/88434/
http://ijeecs.iaescore.com/index.php/IJEECS/article/view/20927
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spelling my.upm.eprints.884342021-12-28T06:54:22Z http://psasir.upm.edu.my/id/eprint/88434/ Development of fall detection and activity recognition using threshold based method and neural network Sai, Siong Jun Harun @ Ramli, Hafiz Rashidi Che Soh, Azura Kamsani, Noor Ain Raja Ahmad, Raja Mohd Kamil Ahmad, Siti Anom Ishak, Asnor Juraiza Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively. Institute of Advanced Engineering and Science 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/88434/1/ABSTRACT.pdf Sai, Siong Jun and Harun @ Ramli, Hafiz Rashidi and Che Soh, Azura and Kamsani, Noor Ain and Raja Ahmad, Raja Mohd Kamil and Ahmad, Siti Anom and Ishak, Asnor Juraiza (2020) Development of fall detection and activity recognition using threshold based method and neural network. Indonesian Journal of Electrical Engineering and Computer Science, 17 (3). 1338 - 1347. ISSN 2502-4752; ESSN: 2502-4760 http://ijeecs.iaescore.com/index.php/IJEECS/article/view/20927 10.11591/ijeecs.v17.i3.pp1338-1347
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
format Article
author Sai, Siong Jun
Harun @ Ramli, Hafiz Rashidi
Che Soh, Azura
Kamsani, Noor Ain
Raja Ahmad, Raja Mohd Kamil
Ahmad, Siti Anom
Ishak, Asnor Juraiza
spellingShingle Sai, Siong Jun
Harun @ Ramli, Hafiz Rashidi
Che Soh, Azura
Kamsani, Noor Ain
Raja Ahmad, Raja Mohd Kamil
Ahmad, Siti Anom
Ishak, Asnor Juraiza
Development of fall detection and activity recognition using threshold based method and neural network
author_facet Sai, Siong Jun
Harun @ Ramli, Hafiz Rashidi
Che Soh, Azura
Kamsani, Noor Ain
Raja Ahmad, Raja Mohd Kamil
Ahmad, Siti Anom
Ishak, Asnor Juraiza
author_sort Sai, Siong Jun
title Development of fall detection and activity recognition using threshold based method and neural network
title_short Development of fall detection and activity recognition using threshold based method and neural network
title_full Development of fall detection and activity recognition using threshold based method and neural network
title_fullStr Development of fall detection and activity recognition using threshold based method and neural network
title_full_unstemmed Development of fall detection and activity recognition using threshold based method and neural network
title_sort development of fall detection and activity recognition using threshold based method and neural network
publisher Institute of Advanced Engineering and Science
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/88434/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/88434/
http://ijeecs.iaescore.com/index.php/IJEECS/article/view/20927
_version_ 1724075509880455168
score 13.211869

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