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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2020
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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 |
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Universiti Malaysia Perlis |
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Malaysia |
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Universiti Malaysia Perlis |
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| 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 |