Publication
Title
Unobtrusive assessment of motor patterns during sleep based on mattress indentation measurements
Author
Abstract
This study investigates how integrated bed measurements can be used to assess motor patterns (movements and postures) during sleep. An algorithm has been developed that detects movements based on the time derivate of mattress surface indentation. After each movement, the algorithm recognizes the adopted sleep posture based on an image feature vector and an optimal separating hyperplane constructed with the theory of support vector machines. The developed algorithm has been tested on a dataset of 30 fully recorded nights in a sleep laboratory. Movement detection has been compared to actigraphy, whereas posture recognition has been validated with a manual posture scoring based on video frames and chest orientation. Results show a high sensitivity for movement detection (91.2%) and posture recognition (between 83.6% and 95.9%), indicating that mattress indentation provides an accurate and unobtrusive measure to assess motor patterns during sleep.
Language
English
Source (journal)
IEEE transactions on information technology in biomedicine / IEEE Engineering in Medicine and Biology Society. - New York
Publication
New York : IEEE, 2011
ISSN
1089-7771
Volume/pages
15:5(2011), p. 787-794
ISI
000294670700013
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 18.11.2011
Last edited 16.07.2017
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