Fall detection and monitoring using machine learning: a comparative study.
The detection of falls has emerged as an important topic for the public to discuss because of the prevalence and severity of unintentional falls, particularly among the elderly. A Fall Detection System, known as an FDS, is a system that gathers data from wearable Internet-of-Things (IoT) device and...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Science and Information Organization
2023
|
Subjects: | |
Online Access: | http://eprints.utm.my/105369/1/ShaimaRMEdeib2023_FallDetectionandMonitoringUsingMachineLearning.pdf http://eprints.utm.my/105369/ http://dx.doi.org/10.14569/IJACSA.2023.0140284 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.105369 |
---|---|
record_format |
eprints |
spelling |
my.utm.1053692024-04-24T06:40:57Z http://eprints.utm.my/105369/ Fall detection and monitoring using machine learning: a comparative study. M. Edeib, Shaima R. Dziyauddin, Rudzidatul Akmam Muhd. Amir, Nur Izdihar T58.6-58.62 Management information systems TA Engineering (General). Civil engineering (General) The detection of falls has emerged as an important topic for the public to discuss because of the prevalence and severity of unintentional falls, particularly among the elderly. A Fall Detection System, known as an FDS, is a system that gathers data from wearable Internet-of-Things (IoT) device and classifies the outcomes to distinguish falls from other activities and call for prompt medical aid in the event of a fall. In this paper, we determine either fall or not fall using machine learning prior to our collected fall dataset from accelerometer sensor. From the acceleration data, the input features are extracted and deployed to supervised machine learning (ML) algorithms namely, Support Vector Machine (SVM), Decision Tree, and Naive Bayes. The results show that the accuracy of fall detection reaches 95%, 97 % and 91% without any false alarms for the SVM, Decision Tree, and Naïve Bayes, respectively Science and Information Organization 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/105369/1/ShaimaRMEdeib2023_FallDetectionandMonitoringUsingMachineLearning.pdf M. Edeib, Shaima R. and Dziyauddin, Rudzidatul Akmam and Muhd. Amir, Nur Izdihar (2023) Fall detection and monitoring using machine learning: a comparative study. International Journal Of Advanced Computer Science And Applications, 14 (2). pp. 723-728. ISSN 2158-107X http://dx.doi.org/10.14569/IJACSA.2023.0140284 DOI: 10.14569/IJACSA.2023.0140284 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
T58.6-58.62 Management information systems TA Engineering (General). Civil engineering (General) |
spellingShingle |
T58.6-58.62 Management information systems TA Engineering (General). Civil engineering (General) M. Edeib, Shaima R. Dziyauddin, Rudzidatul Akmam Muhd. Amir, Nur Izdihar Fall detection and monitoring using machine learning: a comparative study. |
description |
The detection of falls has emerged as an important topic for the public to discuss because of the prevalence and severity of unintentional falls, particularly among the elderly. A Fall Detection System, known as an FDS, is a system that gathers data from wearable Internet-of-Things (IoT) device and classifies the outcomes to distinguish falls from other activities and call for prompt medical aid in the event of a fall. In this paper, we determine either fall or not fall using machine learning prior to our collected fall dataset from accelerometer sensor. From the acceleration data, the input features are extracted and deployed to supervised machine learning (ML) algorithms namely, Support Vector Machine (SVM), Decision Tree, and Naive Bayes. The results show that the accuracy of fall detection reaches 95%, 97 % and 91% without any false alarms for the SVM, Decision Tree, and Naïve Bayes, respectively |
format |
Article |
author |
M. Edeib, Shaima R. Dziyauddin, Rudzidatul Akmam Muhd. Amir, Nur Izdihar |
author_facet |
M. Edeib, Shaima R. Dziyauddin, Rudzidatul Akmam Muhd. Amir, Nur Izdihar |
author_sort |
M. Edeib, Shaima R. |
title |
Fall detection and monitoring using machine learning: a comparative study. |
title_short |
Fall detection and monitoring using machine learning: a comparative study. |
title_full |
Fall detection and monitoring using machine learning: a comparative study. |
title_fullStr |
Fall detection and monitoring using machine learning: a comparative study. |
title_full_unstemmed |
Fall detection and monitoring using machine learning: a comparative study. |
title_sort |
fall detection and monitoring using machine learning: a comparative study. |
publisher |
Science and Information Organization |
publishDate |
2023 |
url |
http://eprints.utm.my/105369/1/ShaimaRMEdeib2023_FallDetectionandMonitoringUsingMachineLearning.pdf http://eprints.utm.my/105369/ http://dx.doi.org/10.14569/IJACSA.2023.0140284 |
_version_ |
1797906005513732096 |
score |
13.160551 |