A theoretic algorithm for fall and motionless detection

• Published in: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare pp. 1 - 6
• Main Authors: Shumei Zhang, McCullagh, P, Nugent, C, Huiru Zheng
• Format: Conference Proceeding
• Language: English
• Published: ICST 01.03.2009
• Subjects: Acceleration; Algorithm design and analysis; Detection algorithms; Fall detection; Force sensors; Gravity; Kinematics; Monitoring; Motion detection; Motionless; Performance evaluation; Phase angle; Robustness; Threshold
• ISBN: 9789639799424; 9639799424
• ISSN: 2153-1633
• DOI: 10.4108/ICST.PERVASIVEHEALTH2009.6034

A 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.