Citation

Kim K, Yun G, Park SK, Kim DH. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2019; 2019: 4611-4614.

Copyright

(Copyright © 2019, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2019.8856698

PMID

31946891

Abstract

In this paper, we propose a new fall detection method that combines 3-axis accelerometer and depth sensors. By combining vision and acceleration-derived features we managed to minimize the false detection rate that is considerably higher when the decision is based on just one type of feature. Also, using machine learning has led to good generalization performance. In addition, we newly created fall database that are more realistic than previous ones. Experiment results show that the proposed method can efficiently detect falls.

Language: en