Literature DB >> 21596711

Detection of falls using accelerometers and mobile phone technology.

Raymond Y W Lee1, Alison J Carlisle.   

Abstract

OBJECTIVES: to study the sensitivity and specificity of fall detection using mobile phone technology.
DESIGN: an experimental investigation using motion signals detected by the mobile phone. SETTING AND PARTICIPANTS: the research was conducted in a laboratory setting, and 18 healthy adults (12 males and 6 females; age = 29 ± 8.7 years) were recruited. MEASUREMENT: each participant was requested to perform three trials of four different types of simulated falls (forwards, backwards, lateral left and lateral right) and eight other everyday activities (sit-to-stand, stand-to-sit, level walking, walking up- and downstairs, answering the phone, picking up an object and getting up from supine). Acceleration was measured using two devices, a mobile phone and an independent accelerometer attached to the waist of the participants.
RESULTS: Bland-Altman analysis shows a higher degree of agreement between the data recorded by the two devices. Using individual upper and lower detection thresholds, the specificity and sensitivity for mobile phone were 0.81 and 0.77, respectively, and for external accelerometer they were 0.82 and 0.96, respectively.
CONCLUSION: fall detection using a mobile phone is a feasible and highly attractive technology for older adults, especially those living alone. It may be best achieved with an accelerometer attached to the waist, which transmits signals wirelessly to a phone.

Entities:  

Mesh:

Year:  2011        PMID: 21596711     DOI: 10.1093/ageing/afr050

Source DB:  PubMed          Journal:  Age Ageing        ISSN: 0002-0729            Impact factor:   10.668


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

Authors:  S Mellone; C Tacconi; L Schwickert; J Klenk; C Becker; L Chiari
Journal:  Z Gerontol Geriatr       Date:  2012-12       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.  Instrumented Test of Sensory Integration for Balance: A Validation Study.

Authors:  Lynn Freeman; Geetanjali Gera; Fay B Horak; Mary T Blackinton; Mark Besch; Laurie King
Journal:  J Geriatr Phys Ther       Date:  2018 Apr/Jun       Impact factor: 3.381

5.  mHealth Assessment and Intervention of Depression and Anxiety in Older Adults.

Authors:  Jason T Grossman; Madelyn R Frumkin; Thomas L Rodebaugh; Eric J Lenze
Journal:  Harv Rev Psychiatry       Date:  2020 May/Jun       Impact factor: 3.732

6.  Mobile Monitoring of Traumatic Brain Injury in Older Adults: Challenges and Opportunities.

Authors:  Andrei Irimia; Susan Wei; Nanshu Lu; Constance M Moore; David N Kennedy
Journal:  Neuroinformatics       Date:  2017-07

7.  Hand, belt, pocket or bag: Practical activity tracking with mobile phones.

Authors:  Stephen A Antos; Mark V Albert; Konrad P Kording
Journal:  J Neurosci Methods       Date:  2013-10-01       Impact factor: 2.390

Review 8.  Older adults and mobile phones for health: a review.

Authors:  Jonathan Joe; George Demiris
Journal:  J Biomed Inform       Date:  2013-06-25       Impact factor: 6.317

9.  Fall classification by machine learning using mobile phones.

Authors:  Mark V Albert; Konrad Kording; Megan Herrmann; Arun Jayaraman
Journal:  PLoS One       Date:  2012-05-07       Impact factor: 3.240

10.  Monitoring functional capability of individuals with lower limb amputations using mobile phones.

Authors:  Mark V Albert; Cliodhna McCarthy; Juliana Valentin; Megan Herrmann; Konrad Kording; Arun Jayaraman
Journal:  PLoS One       Date:  2013-06-04       Impact factor: 3.240

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