NDLI: Individualized detection of ambulatory distress in the field using wearable sensors

Content Provider	IEEE Xplore Digital Library
Author	Williamson, James R. Fischl, Kate Dumas, Andrew Hess, Austin Hughes, Tadd Buller, Mark J.
Copyright Year	2013
Description	Author affiliation: MIT Lincoln Laboratory, Lexington, MA U.S. Army Research Institute of Environmental Medicine (Williamson, James R.; Fischl, Kate; Dumas, Andrew; Hess, Austin; Hughes, Tadd; Buller, Mark J.)
Abstract	The early onset of musculoskeletal injury during ambulation may be detectable due to changes in gait. Body worn accelerometers provide the ability for real-time monitoring and detection of these changes, thereby providing a means for avoiding further injury. We propose algorithms for extracting magnitude and pattern asymmetry features from accelerometers attached to each foot. By registering simultaneous acceleration differences between the two feet, these features provide robustness to a variety of confounding factors, such as changes in walking speed and load carriage. By computing only summary statistics from the acceleration signals, the algorithms can be easily implemented in real-time physiological status monitoring systems. We evaluate the algorithms on a field collection consisting of 32 subjects completing a series of 5 km marches under different loading conditions. We show that changes in the magnitude and pattern asymmetry features are predictive of subject ratings of physical pain and discomfort.
Starting Page	1
Ending Page	6
File Size	1095504
Page Count	6
File Format	PDF
ISBN	9781479903313
ISSN	23251433
e-ISBN	9781479903306
DOI	10.1109/BSN.2013.6575527
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion gait asymmetry Vectors load carriage Hip gait analysis Foot musculoskeletal injury accelerometry feature extraction Feature extraction Acceleration Injuries
Content Type	Text
Resource Type	Article

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