Literature DB >> 18002218

Determination of simple thresholds for accelerometry-based parameters for fall detection.

Maarit Kangas1, Antti Konttila, Ilkka Winblad, Timo Jämsä.   

Abstract

The increasing population of elderly people is mainly living in a home-dwelling environment and needs applications to support their independency and safety. Falls are one of the major health risks that affect the quality of life among older adults. Body attached accelerometers have been used to detect falls. The placement of the accelerometric sensor as well as the fall detection algorithms are still under investigation. The aim of the present pilot study was to determine acceleration thresholds for fall detection, using triaxial accelerometric measurements at the waist, wrist, and head. Intentional falls (forward, backward, and lateral) and activities of daily living (ADL) were performed by two voluntary subjects. The results showed that measurements from the waist and head have potential to distinguish between falls and ADL. Especially, when the simple threshold-based detection was combined with posture detection after the fall, the sensitivity and specificity of fall detection were up to 100 %. On the contrary, the wrist did not appear to be an optimal site for fall detection.

Entities:  

Mesh:

Year:  2007        PMID: 18002218     DOI: 10.1109/IEMBS.2007.4352552

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


  26 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.  Combined Measures of Dynamic Bone Quality and Postural Balance--A Fracture Risk Assessment Approach in Osteoporosis.

Authors:  Amit Bhattacharya; Nelson B Watts; Alok Dwivedi; Rakesh Shukla; Ashutosh Mani; Dima Diab
Journal:  J Clin Densitom       Date:  2015-04-30       Impact factor: 2.617

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.  Older Adults' Perceptions of Fall Detection Devices.

Authors:  Shomir Chaudhuri; Laura Kneale; Thai Le; Elizabeth Phelan; Dori Rosenberg; Hilaire Thompson; George Demiris
Journal:  J Appl Gerontol       Date:  2015-06-24

5.  Accelerometry-based Recognition of the Placement Sites of a Wearable Sensor.

Authors:  Andrea Mannini; Angelo M Sabatini; Stephen S Intille
Journal:  Pervasive Mob Comput       Date:  2015-08-01       Impact factor: 3.453

6.  Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry.

Authors:  Benoit Caby; Suzanne Kieffer; Marie de Saint Hubert; Gerald Cremer; Benoit Macq
Journal:  Biomed Eng Online       Date:  2011-01-09       Impact factor: 2.819

Review 7.  Gait analysis using wearable sensors.

Authors:  Weijun Tao; Tao Liu; Rencheng Zheng; Hutian Feng
Journal:  Sensors (Basel)       Date:  2012-02-16       Impact factor: 3.576

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.  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

10.  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

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