American Journal of Applied Sciences

Elderly People Fall Detection System Using Skeleton Tracking and Recognition

D. Bansal, Abeer Alsadoon, P.W.C. Prasad, Manoranjan Paul and Amr Elchouemi

American Journal of Applied Sciences

Abstract

The fall detection systems use a variety of technologies like sensors, wearable devices, color camera, thermal camera etc. With the use of Microsoft Kinect camera for non-gaming purposes, depth images have started being utilized for fall detection. Various authors have made an attempt at using Kinect for fall detection in combination with a variety of techniques like ellipse analysis, bounding box analysis etc. However, most of these attempts fail to differentiate between human subjects and other inanimate objects and fail to identify the person who has fallen while assuming that there is only one person who needs to be monitored. This paper proposes a new system that is based on the depth images captured by Microsoft Kinect, skeleton tracking and bounding box analysis. The key novelty of this system is that it wraps the moving object into the bounding box and determines the change of size of the moving object by analysis the motion over the time to distinguish the human moving object and non-human moving object. The system stores the joint measurements of the known people in a database and compares the joint measurements of the detected person with the values in the database to identify the person. The proposed solution provides a significantly higher accuracy rate as compared to the current best solution and especially when the person carrying an object, sweeping the floor, dropping an object and picking an object from the floor.

Copyright

© 2018 D. Bansal, Abeer Alsadoon, P.W.C. Prasad, Manoranjan Paul and Amr Elchouemi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.