Kim, Kijung Yun, Guhnoo Park, Sung-Kee Kim, Dong Hwan 2024-01-19T10:37:43Z 2024-01-19T10:37:43Z 2022-03-07 2019-07 1557-170X https://pubs.kist.re.kr/handle/201004/114328 In this paper, we propose a new all 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 ore realistic than previous ones. Experiment results show that the proposed method can efficiently detect falls. English IEEE Fall Detection for the Elderly Based on 3-Axis Accelerometer and Depth Sensor Fusion with Random Forest Classifier Conference 1 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.4611 - 4614 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 4611 4614 US Berlin, GERMANY 2019-07-23 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) 000557295305010 2-s2.0-85077904657