Research Article
A theoretic algorithm for fall and motionless detection
@INPROCEEDINGS{10.4108/ICST.PERVASIVEHEALTH2009.6034, author={Shumei Zhang and Paul McCullagh and Chris Nugent and Huiru Zheng}, title={A theoretic algorithm for fall and motionless detection}, proceedings={3d International ICST Conference on Pervasive Computing Technologies for Healthcare}, proceedings_a={PERVASIVEHEALTH}, year={2009}, month={8}, keywords={Acceleration; Fall detection; Threshold; Phase angle; Motionless.}, doi={10.4108/ICST.PERVASIVEHEALTH2009.6034} }- Shumei Zhang
Paul McCullagh
Chris Nugent
Huiru Zheng
Year: 2009
A theoretic algorithm for fall and motionless detection
PERVASIVEHEALTH
ICST
DOI: 10.4108/ICST.PERVASIVEHEALTH2009.6034
Abstract
robust method of fall and motionless detection is presented. The approach is able to detect falls and motionless periods (standing, sitting, and lying) using only one belt-worn kinematic sensor. The fall detection algorithm analyses the phase changes of vertical acceleration in relation to gravity and impact force using kinematic variables. A phase angle value was used as a threshold to distinguish between falls and normal motion activity. There are two advantages with this approach in comparison with existing approaches: (1) it is computationally efficient and theoretic (2) it is based on a single threshold value which was determined from a kinematic analysis for the falling processes. To evaluate the system, ten subjects were studied each of which performed different types of falls and motionless activities during a period of monitoring activity. These included: normal walking, standing, sitting, lying, a front bend of 90 degrees, tilt over 70 degrees and four kinds of falls (forward, backward, tilt left and right). The results show that 100% of heavy falling, 97% of all falls and 100% of motionless activity were correctly detected in a laboratory environment and the beginning and ends of these events were determined.
