DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK
- https://doi.org/10.1080/18756891.2013.761769How to use a DOI?
- Wearable fall detection system, Tri-axial accelerometer, Classification, Multilayer perceptron
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%.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Hamideh Kerdegari AU - Khairulmizam Samsudin AU - Abdul Rahman Ramli AU - Saeid Mokaram PY - 2013 DA - 2013/01/02 TI - DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK JO - International Journal of Computational Intelligence Systems SP - 127 EP - 136 VL - 6 IS - 1 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.761769 DO - https://doi.org/10.1080/18756891.2013.761769 ID - Kerdegari2013 ER -