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Internet of Things (IoT) Fall Detection using Wearable Sensor

Link to publisher's homepage at https://iopscience.iop.org/issue/1742-6596/1372/1

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Bibliographic Details
Main Authors: Loh Mei, Yee, Lim Chee, Chin, Chong Yen, Fook, Maslia, Dali, Shafriza Nisha, Basah
Other Authors: cclim@unimap.edu.my
Format: Article
Language:English
Published: IOP Publishing 2020
Subjects:
Internet of Things (IoT)
Sensor
Wearable sensor
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69030
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spelling my.unimap-690302020-12-16T08:33:27Z Internet of Things (IoT) Fall Detection using Wearable Sensor Loh Mei, Yee Lim Chee, Chin Chong Yen, Fook Maslia, Dali Shafriza Nisha, Basah cclim@unimap.edu.my Internet of Things (IoT) Sensor Wearable sensor Link to publisher's homepage at https://iopscience.iop.org/issue/1742-6596/1372/1 The IoT fall detection system detects the fall through the data classification of falling and daily living activity. It includes microcontroller board (Arduino Mega 2560), Inertial Measurement Unit sensor (Gy-521 mpu6050) and WI-FI module (ESP8266-01). There total ten (10) subjects in this project. The data of falling and non-falling (daily living activity) can be identified. The falling is the frontward fall, while the daily living activity includes standing, sitting, walking and crouching. K-nearest neighbour (k-NN) classifiers were used in the data classification. The accuracy of k-NN classifiers were 100% between falling and nonfalling class. The feature was selected based on the percentage of accuracy of the k-NN classifier. The features of the Aareal.z (97.14%) and Angle.x (97.24%) were selected due to the good performance during the classification of the falling and non-falling class. The performance of the Aareal.z (58.41%) and Angle.x (57.78%) were satisfactory during the subclassification of the non-falling class. Hence, the feature of Aareal.z and Angle.x were selected as the features which were implemented in the IoT fall detection device. 2020-12-16T08:33:27Z 2020-12-16T08:33:27Z 2019 Article Journal of Physics: Conference Series, vol.1372, 2019, 8 pages 1742-6588 (print) 1742-6596 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69030 https://iopscience.iop.org/issue/1742-6596/1372/1 en International Conference on Biomedical Engineering (ICoBE); IOP Publishing
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Internet of Things (IoT)
Sensor
Wearable sensor
spellingShingle Internet of Things (IoT)
Sensor
Wearable sensor
Loh Mei, Yee
Lim Chee, Chin
Chong Yen, Fook
Maslia, Dali
Shafriza Nisha, Basah
Internet of Things (IoT) Fall Detection using Wearable Sensor
description Link to publisher's homepage at https://iopscience.iop.org/issue/1742-6596/1372/1
author2 cclim@unimap.edu.my
author_facet cclim@unimap.edu.my
Loh Mei, Yee
Lim Chee, Chin
Chong Yen, Fook
Maslia, Dali
Shafriza Nisha, Basah
format Article
author Loh Mei, Yee
Lim Chee, Chin
Chong Yen, Fook
Maslia, Dali
Shafriza Nisha, Basah
author_sort Loh Mei, Yee
title Internet of Things (IoT) Fall Detection using Wearable Sensor
title_short Internet of Things (IoT) Fall Detection using Wearable Sensor
title_full Internet of Things (IoT) Fall Detection using Wearable Sensor
title_fullStr Internet of Things (IoT) Fall Detection using Wearable Sensor
title_full_unstemmed Internet of Things (IoT) Fall Detection using Wearable Sensor
title_sort internet of things (iot) fall detection using wearable sensor
publisher IOP Publishing
publishDate 2020
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69030
_version_ 1698698546133336064
score 13.214268

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