Literature DB >> 17280991

Wearable sensors for reliable fall detection.

Jay Chen1, Karric Kwong, Dennis Chang, Jerry Luk, Ruzena Bajcsy.   

Abstract

Unintentional falls are a common cause of severe injury in the elderly population. By introducing small, non-invasive sensor motes in conjunction with a wireless network, the Ivy Project aims to provide a path towards more independent living for the elderly. Using a small device worn on the waist and a network of fixed motes in the home environment, we can detect the occurrence of a fall and the location of the victim. Low-cost and low-power MEMS accelerometers are used to detect the fall while RF signal strength is used to locate the person.

Entities:  

Year:  2005        PMID: 17280991     DOI: 10.1109/IEMBS.2005.1617246

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  22 in total

Review 1.  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

2.  An environmental-adaptive fall detection system on mobile device.

Authors:  Sung-Yen Chang; Chin-Feng Lai; Han-Chieh Josh Chao; Jong Hyuk Park; Yueh-Min Huang
Journal:  J Med Syst       Date:  2011-03-22       Impact factor: 4.460

3.  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

4.  A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.

Authors:  Omar Aziz; Magnus Musngi; Edward J Park; Greg Mori; Stephen N Robinovitch
Journal:  Med Biol Eng Comput       Date:  2016-04-22       Impact factor: 2.602

5.  Development of a Decision Support Model for Screening Attention-deficit Hyperactivity Disorder with Actigraph-based Measurements of Classroom Activity.

Authors:  H J Kam; Y M Shin; S M Cho; S Y Kim; K W Kim; R W Park
Journal:  Appl Clin Inform       Date:  2010-11-10       Impact factor: 2.342

6.  Mobile psychiatry: towards improving the care for bipolar disorder.

Authors:  Pawel Prociow; Katarzyna Wac; John Crowe
Journal:  Int J Ment Health Syst       Date:  2012-05-29

7.  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

8.  Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems.

Authors:  Mitchell Yuwono; Bruce D Moulton; Steven W Su; Branko G Celler; Hung T Nguyen
Journal:  Biomed Eng Online       Date:  2012-02-16       Impact factor: 2.819

9.  Development of a wearable-sensor-based fall detection system.

Authors:  Falin Wu; Hengyang Zhao; Yan Zhao; Haibo Zhong
Journal:  Int J Telemed Appl       Date:  2015-02-16

Review 10.  Challenges, issues and trends in fall detection systems.

Authors:  Raul Igual; Carlos Medrano; Inmaculada Plaza
Journal:  Biomed Eng Online       Date:  2013-07-06       Impact factor: 2.819

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