Literature DB >> 18002455

Implementation of accelerometer sensor module and fall detection monitoring system based on wireless sensor network.

Youngbum Lee1, Jinkwon Kim, Muntak Son, Myoungho Lee.   

Abstract

This research implements wireless accelerometer sensor module and algorithm to determine wearer's posture, activity and fall. Wireless accelerometer sensor module uses ADXL202, 2-axis accelerometer sensor (Analog Device). And using wireless RF module, this module measures accelerometer signal and shows the signal at ;Acceloger' viewer program in PC. ADL algorithm determines posture, activity and fall that activity is determined by AC component of accelerometer signal and posture is determined by DC component of accelerometer signal. Those activity and posture include standing, sitting, lying, walking, running, etc. By the experiment for 30 subjects, the performance of implemented algorithm was assessed, and detection rate for postures, motions and subjects was calculated. Lastly, using wireless sensor network in experimental space, subject's postures, motions and fall monitoring system was implemented. By the simulation experiment for 30 subjects, 4 kinds of activity, 3 times, fall detection rate was calculated. In conclusion, this system can be application to patients and elders for activity monitoring and fall detection and also sports athletes' exercise measurement and pattern analysis. And it can be expected to common person's exercise training and just plaything for entertainment.

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Year:  2007        PMID: 18002455     DOI: 10.1109/IEMBS.2007.4352789

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  8 in total

1.  Automated detection of near falls: algorithm development and preliminary results.

Authors:  Aner Weiss; Ilan Shimkin; Nir Giladi; Jeffrey M Hausdorff
Journal:  BMC Res Notes       Date:  2010-03-05

Review 2.  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

3.  A fall and near-fall assessment and evaluation system.

Authors:  Anh Dinh; Yang Shi; Daniel Teng; Amitoz Ralhan; Li Chen; Vanina Dal Bello-Haas; Jenny Basran; Seok-Bum Ko; Carl McCrowsky
Journal:  Open Biomed Eng J       Date:  2009-01-21

4.  Sensing movement: microsensors for body motion measurement.

Authors:  Hansong Zeng; Yi Zhao
Journal:  Sensors (Basel)       Date:  2011-01-10       Impact factor: 3.576

5.  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

Review 6.  Automatic fall monitoring: a review.

Authors:  Natthapon Pannurat; Surapa Thiemjarus; Ekawit Nantajeewarawat
Journal:  Sensors (Basel)       Date:  2014-07-18       Impact factor: 3.576

Review 7.  Ambient Sensors for Elderly Care and Independent Living: A Survey.

Authors:  Md Zia Uddin; Weria Khaksar; Jim Torresen
Journal:  Sensors (Basel)       Date:  2018-06-25       Impact factor: 3.576

8.  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

  8 in total

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