Literature DB >> 17272157

Preliminary evaluation of a full-time falling monitor for the elderly.

A Diaz1, M Prado, L M Roa, J Reina-Tosina, G Sanchez.   

Abstract

The article presents the early outcomes of the evaluation of an intelligent accelerometer unit (IAU) utilized for detecting the falling events of elderly people . The overall design of the monitor where the IAU is integrated is briefly exposed. The outcomes of a laboratory study carried out over 8 volunteers show that the device is able to distinguish true falling events from normal activities like fast walking or going up/downstairs. The influences of the subject and the environment have been taken into account profiting from the processing capacity of the monitor distributed architecture.

Entities:  

Year:  2004        PMID: 17272157     DOI: 10.1109/IEMBS.2004.1403637

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


  9 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.  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

Review 3.  Fall detection devices and their use with older adults: a systematic review.

Authors:  Shomir Chaudhuri; Hilaire Thompson; George Demiris
Journal:  J Geriatr Phys Ther       Date:  2014 Oct-Dec       Impact factor: 3.381

4.  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

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

Authors:  Guoru Zhao; Zhanyong Mei; Ding Liang; Kamen Ivanov; Yanwei Guo; Yongfeng Wang; Lei Wang
Journal:  Sensors (Basel)       Date:  2012-11-08       Impact factor: 3.576

6.  Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network.

Authors:  Shuai Tao; Mineichi Kudo; Hidetoshi Nonaka
Journal:  Sensors (Basel)       Date:  2012-12-07       Impact factor: 3.576

7.  Simple fall criteria for MEMS sensors: data analysis and sensor concept.

Authors:  Alwathiqbellah Ibrahim; Mohammad I Younis
Journal:  Sensors (Basel)       Date:  2014-07-08       Impact factor: 3.576

8.  Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing.

Authors:  Juliet A M Haarman; Erik Maartens; Herman van der Kooij; Jaap H Buurke; Jasper Reenalda; Johan S Rietman
Journal:  J Neuroeng Rehabil       Date:  2017-12-02       Impact factor: 4.262

9.  Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.

Authors:  Omar Aziz; Jochen Klenk; Lars Schwickert; Lorenzo Chiari; Clemens Becker; Edward J Park; Greg Mori; Stephen N Robinovitch
Journal:  PLoS One       Date:  2017-07-05       Impact factor: 3.240

  9 in total

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