Citation

de Miguel K, Brunete A, Hernando M, Gambao E. Sensors (Basel) 2017; 17(12): s17122864.

Affiliation

Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain. ernesto.gambao@upm.es.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s17122864

PMID

29232846

Abstract

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Language: en

Keywords

camera-based; elderly; fall detection; home automation