Document Detail


Quantitative falls risk estimation through multi-sensor assessment of standing balance.
MedLine Citation:
PMID:  23151494     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. Measures of postural stability have been associated with the incidence of falls in older adults. The aim of this study was to develop a model that accurately classifies fallers and non-fallers using novel multi-sensor quantitative balance metrics that can be easily deployed into a home or clinic setting. We compared the classification accuracy of our model with an established method for falls risk assessment, the Berg balance scale. Data were acquired using two sensor modalities-a pressure sensitive platform sensor and a body-worn inertial sensor, mounted on the lower back-from 120 community dwelling older adults (65 with a history of falls, 55 without, mean age 73.7 ± 5.8 years, 63 female) while performing a number of standing balance tasks in a geriatric research clinic. Results obtained using a support vector machine yielded a mean classification accuracy of 71.52% (95% CI: 68.82-74.28) in classifying falls history, obtained using one model classifying all data points. Considering male and female participant data separately yielded classification accuracies of 72.80% (95% CI: 68.85-77.17) and 73.33% (95% CI: 69.88-76.81) respectively, leading to a mean classification accuracy of 73.07% in identifying participants with a history of falls. Results compare favourably to those obtained using the Berg balance scale (mean classification accuracy: 59.42% (95% CI: 56.96-61.88)). Results from the present study could lead to a robust method for assessing falls risk in both supervised and unsupervised environments.
Authors:
Barry R Greene; Denise McGrath; Lorcan Walsh; Emer P Doheny; David McKeown; Chiara Garattini; Clodagh Cunningham; Lisa Crosby; Brian Caulfield; Rose A Kenny
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-15
Journal Detail:
Title:  Physiological measurement     Volume:  33     ISSN:  1361-6579     ISO Abbreviation:  Physiol Meas     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9306921     Medline TA:  Physiol Meas     Country:  -    
Other Details:
Languages:  ENG     Pagination:  2049-2063     Citation Subset:  -    
Affiliation:
Technology Research for Independent Living (TRIL), Dublin, Ireland. Applied Technology and Design, Intel Labs, Leixlip, Co. Kildare, Ireland.
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