Zhang, Shumei, McCullagh, PJ, Nugent, CD and Zheng, H (2009) A theoretic algorithm for fall and motionless detection. In: Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference, London. IEEE. 6 pp. [Conference contribution]
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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.
|Item Type:||Conference contribution (Paper)|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Mathematics
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Smart Environments
|Deposited By:||Dr Paul McCullagh|
|Deposited On:||12 Apr 2010 16:31|
|Last Modified:||09 Dec 2015 10:45|
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