Literature DB >> 21424848

An environmental-adaptive fall detection system on mobile device.

Sung-Yen Chang1, Chin-Feng Lai, Han-Chieh Josh Chao, Jong Hyuk Park, Yueh-Min Huang.   

Abstract

When facing damages caused by falls, a well designed smart sensor system to detect falls can be both medically and economically helpful. This research introduces a portable terrain adaptable fall detection system, by placing accelerometers and gyroscopes in parts of the body and transmit data through wireless transmitter modules to mobile devices to get the related information and combining it with the center of gravity clustering algorithm introduced in this research which computes the human body behavior patterns according the relationship between the center of gravity in the body and the feet portion of the body. Compared with the research in the past, this system is not only highly accurate and robust, but also able to adapt to different types of terrains, which solves the problems that other researches have for detection errors when the client is climbing the stairs or walking on a slant.

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Year:  2011        PMID: 21424848     DOI: 10.1007/s10916-011-9677-2

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  8 in total

1.  A comprehensive survey of Wireless Body Area Networks : on PHY, MAC, and Network layers solutions.

Authors:  Sana Ullah; Henry Higgins; Bart Braem; Benoit Latre; Chris Blondia; Ingrid Moerman; Shahnaz Saleem; Ziaur Rahman; Kyung Sup Kwak
Journal:  J Med Syst       Date:  2010-08-19       Impact factor: 4.460

2.  Evaluation of a fall detector based on accelerometers: a pilot study.

Authors:  U Lindemann; A Hock; M Stuber; W Keck; C Becker
Journal:  Med Biol Eng Comput       Date:  2005-09       Impact factor: 2.602

3.  Monocular 3D head tracking to detect falls of elderly people.

Authors:  Caroline Rougier; Jean Meunier; Alain St-Arnaud; Jacqueline Rousseau
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

4.  Wearable sensors for reliable fall detection.

Authors:  Jay Chen; Karric Kwong; Dennis Chang; Jerry Luk; Ruzena Bajcsy
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2005

5.  Portable preimpact fall detector with inertial sensors.

Authors:  Ge Wu; Shuwan Xue
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2008-04       Impact factor: 3.802

6.  Body Area Networks for ubiquitous healthcare applications: opportunities and challenges.

Authors:  Emil Jovanov; Aleksandar Milenkovic
Journal:  J Med Syst       Date:  2011-02-17       Impact factor: 4.460

7.  The costs of fatal and non-fatal falls among older adults.

Authors:  J A Stevens; P S Corso; E A Finkelstein; T R Miller
Journal:  Inj Prev       Date:  2006-10       Impact factor: 2.399

8.  Data acquisition system using six degree-of-freedom inertia sensor and ZigBee wireless link for fall detection and prevention.

Authors:  A Dinh; D Teng; L Chen; S B Ko; Y Shi; J Basran; V Del Bello-Hass
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008
  8 in total
  7 in total

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

Authors:  Shih-Yeh Chen; Chin-Feng Lai; Ren-Hung Hwang; Ying-Hsun Lai; Ming-Shi Wang
Journal:  J Med Syst       Date:  2015-10-21       Impact factor: 4.460

Review 2.  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

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.  An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

Authors:  Zhen Li; Zhiqiang Wei; Yaofeng Yue; Hao Wang; Wenyan Jia; Lora E Burke; Thomas Baranowski; Mingui Sun
Journal:  J Med Syst       Date:  2015-03-19       Impact factor: 4.460

5.  Smartphone-based solutions for fall detection and prevention: challenges and open issues.

Authors:  Mohammad Ashfak Habib; Mas S Mohktar; Shahrul Bahyah Kamaruzzaman; Kheng Seang Lim; Tan Maw Pin; Fatimah Ibrahim
Journal:  Sensors (Basel)       Date:  2014-04-22       Impact factor: 3.576

Review 6.  Analysis of Android Device-Based Solutions for Fall Detection.

Authors:  Eduardo Casilari; Rafael Luque; María-José Morón
Journal:  Sensors (Basel)       Date:  2015-07-23       Impact factor: 3.576

7.  Detecting falls with wearable sensors using machine learning techniques.

Authors:  Ahmet Turan Özdemir; Billur Barshan
Journal:  Sensors (Basel)       Date:  2014-06-18       Impact factor: 3.576

  7 in total

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