Citation
Kerdegari, Hamideh and Samsudin, Khairulmizam and Ramli, Abdul Rahman and Ghotoorlar, Saeid Mokaram
(2013)
Development of wearable human fall detection system using multilayer perceptron neural network.
International Journal of Computational Intelligence Systems, 6 (1).
pp. 127-136.
ISSN 1875-6891; ESSN: 1875-6883
Abstract
This paper presents an accurate wearable fall detection system which can identify the occurrence of falls among elderly population. A waist worn tri-axial accelerometer was used to capture the movement signals of human body. A set of laboratory-based falls and activities of daily living (ADL) were performed by volunteers with different physical characteristics. The collected acceleration patterns were classified precisely to fall and ADL using multilayer perceptron (MLP) neural network. This work was resulted to a high accuracy wearable fall-detection system with the accuracy of 91.6%.
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Official URL or Download Paper: https://www.tandfonline.com/doi/abs/10.1080/187568...
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Additional Metadata
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Engineering |
| DOI Number: | https://doi.org/10.1080/18756891.2013.761769 |
| Publisher: | Atlantis Press and Taylor & Francis |
| Keywords: | Wearable fall detection system; Tri-axial accelerometer; Classification; Multilayer perceptron |
| Depositing User: | Nabilah Mustapa |
| Date Deposited: | 08 Jul 2019 07:30 |
| Last Modified: | 08 Jul 2019 07:30 |
| Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/18756891.2013.761769 |
| URI: | http://psasir.upm.edu.my/id/eprint/15345 |
| Statistic Details: | View Download Statistic |
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