Literature DB >> 10940409

Distinguishing fall activities from normal activities by velocity characteristics.

G Wu1.   

Abstract

The purpose of this study was to identify unique features of the velocity profile during normal and abnormal (i.e., fall) activities so as to make the automatic detection of falls during the descending phase of a fall possible. Normal activities included walking, rising from a chair and sitting down, descending stairs, picking up an object from the floor, transferring in and out of a tub, and lying down on a bed. The fall activities included tripping, forward and backward falls from standing. The horizontal and vertical velocities (V(h) and V(v)) at various locations of the trunk was measured. It was found that the V(h) and V(v) of the trunk during normal activities were within a well-controlled range, and that when the velocity in one direction increased, the velocity in the other direction usually did not. In contrast, the V(h) and V(v) demonstrated two different characteristics for the fall movement. Firstly, the magnitude of both V(h) and V(v) of the trunk increased dramatically during the falling phase, reaching up to 2-3 times that of normal velocities. Secondly, the increase of V(h) and V(v) magnitude usually occurred simultaneously, and usually about 300-400 ms before the end of the fall. These two velocity characteristics, that is, the magnitude change and the timing of the magnitude change of both V(h) and V(v), could be used to distinguish fall movements from normal activities during the descending phase of the fall. It is hoped that the application of these two velocity characteristics could lead to potentially preventing or degrading fall-related injuries in the elderly population when connected with other devices.

Mesh:

Year:  2000        PMID: 10940409     DOI: 10.1016/s0021-9290(00)00117-2

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  12 in total

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3.  Development and evaluation of a prior-to-impact fall event detection algorithm.

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4.  Evaluation of accelerometer-based fall detection algorithms on real-world falls.

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5.  A distributed multiagent system architecture for body area networks applied to healthcare monitoring.

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Journal:  Biomed Res Int       Date:  2015-03-22       Impact factor: 3.411

Review 6.  Automatic fall monitoring: a review.

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7.  "SmartMonitor"--an intelligent security system for the protection of individuals and small properties with the possibility of home automation.

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8.  Exploration and implementation of a pre-impact fall recognition method based on an inertial body sensor network.

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Journal:  Sensors (Basel)       Date:  2012-11-08       Impact factor: 3.576

9.  Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm.

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Review 10.  Sudden event recognition: a survey.

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Journal:  Sensors (Basel)       Date:  2013-08-05       Impact factor: 3.576

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