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Author Aziz, Omar ♦ Musngi, Magnus ♦ Park, Edward J. ♦ Mori, Greg ♦ Robivitch, Stephen N.
Source SpringerLink
Content type Text
Publisher Springer Berlin Heidelberg
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Technology ♦ Medicine & health
Subject Keyword Older adults ♦ Falls ♦ Wearable sensors ♦ Machine learning ♦ Threshold-based algorithms ♦ Human Physiology ♦ Biomedical Engineering ♦ Imaging ♦ Radiology ♦ Computer Applications
Abstract Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the ‘long lie’ experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.
ISSN 01400118
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-04-22
Publisher Place Berlin, Heidelberg
e-ISSN 17410444
Journal Medical and Biological Engineering and Computing
Volume Number 55
Issue Number 1
Page Count 11
Starting Page 45
Ending Page 55


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Source: SpringerLink