Body Falling Gesture Recognition Based on SOM and Triaxial Acceleration Information

DOI

10.2991/cimns-16.2016.18How to use a DOI?

Keywords

human fall gesture recognition; self-organizing map (SOM); triaxial acceleration sensor

Abstract

In order to improve the performance of fall detection system for the elderly based on triaxial acceleration sensor, and accurately to judge the fall direction of human body, a method was put forward based on self-organizing map neural network (SOM) and the information of triaxial acceleration sensor to cluster and analyze the human motion. To verify the recognition results of the SOM method, 130 samples of 13 common action including fall were participated in the SOM network testing. The results show that the sensitivity, specificity and accuracy of the new system were 90%, 96.7%, 94.6%, respectively. These results were better than those of the method of threshold value.

Copyright

© 2016, 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/).

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TY  - CONF
AU  - Hongbo Chen
AU  - Qing Gao
AU  - Tao Feng
AU  - Yu Liu
AU  - Xinhua Xiao
PY  - 2016/09
DA  - 2016/09
TI  - Body Falling Gesture Recognition Based on SOM and Triaxial Acceleration Information
BT  - Proceedings of the 2016 International Conference on Communications, Information Management and Network Security
PB  - Atlantis Press
SP  - 70
EP  - 73
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-16.2016.18
DO  - 10.2991/cimns-16.2016.18
ID  - Chen2016/09
ER  -