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.
LanguageEnglish
Title of host publicationUnknown Host Publication
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 → …

Fingerprint

kinematics
tilt
walking
gravity
sensor
monitoring
detection
method
laboratory
comparison
threshold value
analysis

Cite this

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title = "A theoretic algorithm for fall and motionless detection",
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.",
author = "Shumei Zhang and PJ McCullagh and CD Nugent and H Zheng",
note = "Reference text: This paper appears in: Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference on On page(s): 1 - 6 Location: London, UK Print ISBN: 978-963-9799-42-4 Digital Object Identifier: 10.4108/ICST.PERVASIVEHEALTH2009.6034 Current Version Published: 04 August 2009",
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Zhang, S, McCullagh, PJ, Nugent, CD & Zheng, H 2009, A theoretic algorithm for fall and motionless detection. in Unknown Host Publication. pp. 1-6, Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference, 4/08/09. https://doi.org/10.4108/ICST.PERVASIVEHEALTH2009.6034

A theoretic algorithm for fall and motionless detection. / Zhang, Shumei; McCullagh, PJ; Nugent, CD; Zheng, H.

Unknown Host Publication. 2009. p. 1-6.

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

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N2 - 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.

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