Literature DB >> 19163297

A wearable triaxial accelerometry system for longitudinal assessment of falls risk.

Michael R Narayanan1, Maria Elena Scalzi, Stephen J Redmond, Steven R Lord, Branko G Celler, Nigel H Lovell.   

Abstract

Falls-related injuries in the elderly population are a major cause of morbidity and represent one of the most significant contributors to hospitalizations and rising health care expense in developed countries. Many laboratory-based studies have described falls detection systems using wearable accelerometry. However, only a limited number of reports have tried to address the difficult issues of falls detection and falls prevention in unsupervised or free-living environments. We describe a waist-mounted triaxial accelerometry (Triax) system with a remote data collection capability to provide unsupervised monitoring of the elderly. The basis of the monitoring is a self-administered directed-routine (DR) comprising three separate tests measured by way of the Triax. We present an initial evaluation of the DR results in 36 patients to detect early changes in functional ability and facilitate falls risk stratification. Extracted features considered alone show a correlation with falls risk of approximately rho=0.5. Estimation of falls risk using a linear least squares model provides a root-mean-squared error of 0.69 (rho=0.58, p<0.0002).

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Year:  2008        PMID: 19163297     DOI: 10.1109/IEMBS.2008.4649794

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  7 in total

1.  Accelerometer's position independent physical activity recognition system for long-term activity monitoring in the elderly.

Authors:  Adil Mehmood Khan; Young-Koo Lee; Sungyoung Lee; Tae-Seong Kim
Journal:  Med Biol Eng Comput       Date:  2010-11-04       Impact factor: 2.602

2.  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

3.  Human Activity Recognition from Body Sensor Data using Deep Learning.

Authors:  Mohammad Mehedi Hassan; Shamsul Huda; Md Zia Uddin; Ahmad Almogren; Majed Alrubaian
Journal:  J Med Syst       Date:  2018-04-16       Impact factor: 4.460

Review 4.  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

5.  Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients.

Authors:  Michael Marschollek; Anja Rehwald; Klaus-Hendrik Wolf; Matthias Gietzelt; Gerhard Nemitz; Hubertus Meyer zu Schwabedissen; Mareike Schulze
Journal:  BMC Med Inform Decis Mak       Date:  2011-06-28       Impact factor: 2.796

6.  Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson's Disease.

Authors:  Arash Atrsaei; Clint Hansen; Morad Elshehabi; Susanne Solbrig; Daniela Berg; Inga Liepelt-Scarfone; Walter Maetzler; Kamiar Aminian
Journal:  Front Aging Neurosci       Date:  2021-11-30       Impact factor: 5.750

Review 7.  Review of fall risk assessment in geriatric populations using inertial sensors.

Authors:  Jennifer Howcroft; Jonathan Kofman; Edward D Lemaire
Journal:  J Neuroeng Rehabil       Date:  2013-08-08       Impact factor: 4.262

  7 in total

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