Development of a wearable human fall detection system
Fall contributes for over 80% of injury-related hospitalisation, especially amongst the elderly. A long lie due to a fall may lead to other health complications if medical intervention does not take place in a timely manner. Hence, a fall detection system that employs a wearable detector is importan...
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| Format: | Article |
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Malaysia Board of Technologists (MBOT)
2022
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| _version_ | 1796983925920235520 |
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| author | Chua, Kean Hong Sai, Siong Jun Chan, Chi Yen Kamsani, Noor Ain Che Soh, Azura Raja Ahmad, Raja Mohd Kamil |
| author_facet | Chua, Kean Hong Sai, Siong Jun Chan, Chi Yen Kamsani, Noor Ain Che Soh, Azura Raja Ahmad, Raja Mohd Kamil |
| author_sort | Chua, Kean Hong |
| collection | UPM |
| description | Fall contributes for over 80% of injury-related hospitalisation, especially amongst the elderly. A long lie due to a fall may lead to other health complications if medical intervention does not take place in a timely manner. Hence, a fall detection system that employs a wearable detector is important to detect the fall automatically. In this study, an accelerometer and gyroscope sensors were installed in a wearable fall detector. A fall detection algorithm was developed using MATLAB. This algorithm will extract features from the signal sent by the sensors and conduct a series of decision-making and classification process to determine whether a fall has actually occurred. The accuracy of the fall detection algorithm was determined to be at 98.41%. The detected fall will be notified to the elderly person’s smart phone via Bluetooth and the smart phone will send an emergency message to the caregiver’s smart phone via the Google Cloud Messaging (GCM) system. The smart phone will also update the database, with regards to the fall event. This system embraces the Internet of Things (IoT), big data, and data analytics concepts as well as allows data-driven healthcare to be developed. |
| first_indexed | 2024-03-06T11:13:19Z |
| format | Article |
| id | upm.eprints-100603 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:13:19Z |
| publishDate | 2022 |
| publisher | Malaysia Board of Technologists (MBOT) |
| record_format | dspace |
| spelling | upm.eprints-1006032023-10-09T18:44:59Z http://psasir.upm.edu.my/id/eprint/100603/ Development of a wearable human fall detection system Chua, Kean Hong Sai, Siong Jun Chan, Chi Yen Kamsani, Noor Ain Che Soh, Azura Raja Ahmad, Raja Mohd Kamil Fall contributes for over 80% of injury-related hospitalisation, especially amongst the elderly. A long lie due to a fall may lead to other health complications if medical intervention does not take place in a timely manner. Hence, a fall detection system that employs a wearable detector is important to detect the fall automatically. In this study, an accelerometer and gyroscope sensors were installed in a wearable fall detector. A fall detection algorithm was developed using MATLAB. This algorithm will extract features from the signal sent by the sensors and conduct a series of decision-making and classification process to determine whether a fall has actually occurred. The accuracy of the fall detection algorithm was determined to be at 98.41%. The detected fall will be notified to the elderly person’s smart phone via Bluetooth and the smart phone will send an emergency message to the caregiver’s smart phone via the Google Cloud Messaging (GCM) system. The smart phone will also update the database, with regards to the fall event. This system embraces the Internet of Things (IoT), big data, and data analytics concepts as well as allows data-driven healthcare to be developed. Malaysia Board of Technologists (MBOT) 2022-12-29 Article PeerReviewed Chua, Kean Hong and Sai, Siong Jun and Chan, Chi Yen and Kamsani, Noor Ain and Che Soh, Azura and Raja Ahmad, Raja Mohd Kamil (2022) Development of a wearable human fall detection system. Journal of Emerging Technologies and Industrial Applications, 1 (2). pp. 1-13. ISSN 2948-507X https://jetia.mbot.org.my/index.php/jetia/article/view/14 |
| spellingShingle | Chua, Kean Hong Sai, Siong Jun Chan, Chi Yen Kamsani, Noor Ain Che Soh, Azura Raja Ahmad, Raja Mohd Kamil Development of a wearable human fall detection system |
| title | Development of a wearable human fall detection system |
| title_full | Development of a wearable human fall detection system |
| title_fullStr | Development of a wearable human fall detection system |
| title_full_unstemmed | Development of a wearable human fall detection system |
| title_short | Development of a wearable human fall detection system |
| title_sort | development of a wearable human fall detection system |
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