Open AccessEvent‐driven system for fall detection using body‐worn accelerometer and depth sensor
Author(s) -
Kepski Michal,
Kwolek Bogdan
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0119
Subject(s) - accelerometer , computer science , computer vision , artificial intelligence , event (particle physics) , sensitivity (control systems) , tracking (education) , inertial measurement unit , wearable computer , accidental fall , set (abstract data type) , reliability (semiconductor) , engineering , embedded system , medicine , psychology , pedagogy , programming language , power (physics) , physics , surgery , quantum mechanics , electronic engineering , operating system
The authors present efficient and effective algorithms for fall detection on the basis of sequences of depth maps and data from a wireless inertial sensor worn by a monitored person. A set of descriptors is discussed to permit distinguishing between accidental falls and activities of daily living. Experimental validation is carried out on the freely available dataset consisting of synchronised depth and accelerometric data. Extensive experiments are conducted in the scenario with a static camera facing the scene and an active camera observing the same scene from above. Several experiments consisting of person detection, tracking and fall detection in real‐time are carried out to show efficiency and reliability of the proposed solutions. The experimental results show that the developed algorithms for fall detection have high sensitivity and specificity.