Literature DB >> 16406739

Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization.

M N Nyan1, F E H Tay, A W Y Tan, K H W Seah.   

Abstract

Distinguishing sideways and backward falls from normal activities of daily living using angular rate sensors (gyroscopes) was explored in this paper. Gyroscopes were secured on a shirt at the positions of sternum (S), front of the waist (FW) and right underarm (RU) to measure angular rate in lateral and sagittal planes of the body during falls and normal activities. Moreover, the motions of the fall incidents were captured by a high-speed camera at a frame rate of 250 frames per second (fps) to study the body configuration during fall. The high-speed camera and the sensor data capture system were activated simultaneously to synchronize the picture frame of high-speed camera and the sensor data. The threshold level for each sensor was set to distinguish fall activities from normal activities. Lead time of fall activities (time after threshold value is surpassed to the time when the hip hits the ground) and relative angle of body configuration (angle beta between the vertical line and the line from the center point of the foot or the center point between the two legs to that of the waist) at the threshold level were studied. For sideways falls, lead times of sensors at positions FW and S were about 200-220ms and 135-182ms, respectively. The lead time of the slippery backward fall (about 98ms) from the sensor at position RU was shorter than that of the sideways falls from the sensors at positions FW and S. The relative angle of body configuration at threshold level for sideways and backward falls were about 40-43 degrees for the sensor at position FW, about 43-52 degrees for the sensor at position S and about 54 degrees for the sensor at position RU, respectively. This is the first study that investigates fall dynamics in detection of fall before the person hits the ground using angular rate sensors (gyroscopes).

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Year:  2006        PMID: 16406739     DOI: 10.1016/j.medengphy.2005.11.008

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  15 in total

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2.  An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services.

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Review 3.  Fall detection with body-worn sensors : a systematic review.

Authors:  L Schwickert; C Becker; U Lindemann; C Maréchal; A Bourke; L Chiari; J L Helbostad; W Zijlstra; K Aminian; C Todd; S Bandinelli; J Klenk
Journal:  Z Gerontol Geriatr       Date:  2013-12       Impact factor: 1.281

4.  An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans.

Authors:  Omar Aziz; Stephen N Robinovitch
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-08-22       Impact factor: 3.802

5.  Trunk angular kinematics during slip-induced backward falls and activities of daily living.

Authors:  Jian Liu; Thurmon E Lockhart
Journal:  J Biomech Eng       Date:  2014-10       Impact factor: 2.097

Review 6.  Fall detection devices and their use with older adults: a systematic review.

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Journal:  J Geriatr Phys Ther       Date:  2014 Oct-Dec       Impact factor: 3.381

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

Authors:  Jian Liu; Thurmon E Lockhart
Journal:  IEEE Trans Biomed Eng       Date:  2014-04-04       Impact factor: 4.538

8.  Local Dynamic Stability Assessment of Motion Impaired Elderly Using Electronic Textile Pants.

Authors:  Jian Liu; Thurmon E Lockhart; Mark Jones; Tom Martin
Journal:  IEEE Trans Autom Sci Eng       Date:  2008-10       Impact factor: 5.083

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

Authors:  Fabio Bagalà; Clemens Becker; Angelo Cappello; Lorenzo Chiari; Kamiar Aminian; Jeffrey M Hausdorff; Wiebren Zijlstra; Jochen Klenk
Journal:  PLoS One       Date:  2012-05-16       Impact factor: 3.240

10.  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

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