A theoretic algorithm for fall and motionless detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 4 Aug 2009
EventPervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference - London
Duration: 4 Aug 2009 → …

Conference

ConferencePervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference
Period4/08/09 → …

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