Literature DB >> 23184298

Smartphone-based solutions for fall detection and prevention: the FARSEEING approach.

S Mellone1, C Tacconi, L Schwickert, J Klenk, C Becker, L Chiari.   

Abstract

Falls are not an inevitable consequence of aging. The risk and rate of falls can be reduced. Recent improvements in smartphone technology enable implementation of a wide variety of services and applications, thus making the smartphone more of a digital companion than simply a communication tool. This paper presents the results obtained by the FARSEEING project where smartphones are one example of intervention in a population-based scenario. The applications developed take advantage of the smartphone-embedded inertial sensors and require that subjects wear the smartphone by means of a waist belt. The uFall Android application has been developed for monitoring the user's motor activities at home. The application does not require any direct interaction with the user and it is also capable of running a real-time fall-detection algorithm. uTUG is a stand-alone application for instrumenting the Timed Up and Go test, which is a test often included in fall risk assessment protocols. The application acts like a pocket-sized motion laboratory, since it is capable not only of recording the trial but also of processing the data and immediately displaying the results. uTUG is designed to be self-administrable at home.

Mesh:

Year:  2012        PMID: 23184298     DOI: 10.1007/s00391-012-0404-5

Source DB:  PubMed          Journal:  Z Gerontol Geriatr        ISSN: 0948-6704            Impact factor:   1.281


  22 in total

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2.  Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities.

Authors:  A K Bourke; P van de Ven; M Gamble; R O'Connor; K Murphy; E Bogan; E McQuade; P Finucane; G Olaighin; J Nelson
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3.  Comparison of low-complexity fall detection algorithms for body attached accelerometers.

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Journal:  Gait Posture       Date:  2008-02-21       Impact factor: 2.840

4.  An acoustic fall detector system that uses sound height information to reduce the false alarm rate.

Authors:  Mihail Popescu; Yun Li; Marjorie Skubic; Marilyn Rantz
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

5.  The patient who falls: "It's always a trade-off".

Authors:  Mary E Tinetti; Chandrika Kumar
Journal:  JAMA       Date:  2010-01-20       Impact factor: 56.272

6.  iFall: an Android application for fall monitoring and response.

Authors:  Frank Sposaro; Gary Tyson
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

Review 7.  Interventions for preventing falls in older people in nursing care facilities and hospitals.

Authors:  Ian D Cameron; Geoff R Murray; Lesley D Gillespie; M Clare Robertson; Keith D Hill; Robert G Cumming; Ngaire Kerse
Journal:  Cochrane Database Syst Rev       Date:  2010-01-20

Review 8.  Interventions for preventing falls in older people living in the community.

Authors:  Lesley D Gillespie; M Clare Robertson; William J Gillespie; Sarah E Lamb; Simon Gates; Robert G Cumming; Brian H Rowe
Journal:  Cochrane Database Syst Rev       Date:  2009-04-15

9.  Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements.

Authors:  M Marschollek; G Nemitz; M Gietzelt; K H Wolf; H Meyer Zu Schwabedissen; R Haux
Journal:  Z Gerontol Geriatr       Date:  2009-06-20       Impact factor: 1.281

10.  The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson's disease.

Authors:  Cris Zampieri; Arash Salarian; Patricia Carlson-Kuhta; Kamiar Aminian; John G Nutt; Fay B Horak
Journal:  J Neurol Neurosurg Psychiatry       Date:  2009-09-02       Impact factor: 10.154

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  33 in total

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Authors:  P Benzinger; U Lindemann; C Becker; K Aminian; M Jamour; S E Flick
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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.  Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors.

Authors:  C Becker; L Schwickert; S Mellone; F Bagalà; L Chiari; J L Helbostad; W Zijlstra; K Aminian; A Bourke; C Todd; S Bandinelli; N Kerse; J Klenk
Journal:  Z Gerontol Geriatr       Date:  2012-12       Impact factor: 1.281

Review 4.  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

5.  Sensitivity of fNIRS measurement to head motion: an applied use of smartphones in the lab.

Authors:  Xu Cui; Joseph M Baker; Ning Liu; Allan L Reiss
Journal:  J Neurosci Methods       Date:  2015-02-14       Impact factor: 2.390

Review 6.  Toward Automating Clinical Assessments: A Survey of the Timed Up and Go.

Authors:  Gina Sprint; Diane J Cook; Douglas L Weeks
Journal:  IEEE Rev Biomed Eng       Date:  2015-01-12

7.  Advances in Long Term Physical Behaviour Monitoring.

Authors:  Jorunn L Helbostad; Lorenzo Chiari; Sebastien Chastin; Kamiar Aminian
Journal:  Biomed Res Int       Date:  2016-04-11       Impact factor: 3.411

8.  Feasibility, reliability, and validity of a smartphone based application for the assessment of cognitive function in the elderly.

Authors:  Robert M Brouillette; Heather Foil; Stephanie Fontenot; Anthony Correro; Ray Allen; Corby K Martin; Annadora J Bruce-Keller; Jeffrey N Keller
Journal:  PLoS One       Date:  2013-06-11       Impact factor: 3.240

Review 9.  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

Review 10.  Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement.

Authors:  Michael B del Rosario; Stephen J Redmond; Nigel H Lovell
Journal:  Sensors (Basel)       Date:  2015-07-31       Impact factor: 3.576

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