TY  - JOUR
PY  - 2019//
TI  - Fall detection for the elderly based on 3-axis accelerometer and depth sensor fusion with random forest classifier
JO  - Conference proceedings - IEEE engineering in medicine and biology society
A1  - Kim, Kijung
A1  - Yun, Guhnoo
A1  - Park, Sung-Kee
A1  - Kim, Dong Hwan
SP  - 4611
EP  - 4614
VL  - 2019
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1557-170X
UR  - http://dx.doi.org/10.1109/EMBC.2019.8856698
ID  - ref1
ER  -