title: Unobtrusive assessment of motor patterns during sleep based on mattress indentation measurements.
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.
authors: Division of Biomechanics and Engineering Design, Katholieke Universiteit Leuven, Leuven, Belgium. vincent.verhaert@mech.kuleuven.be
authors: Vincent
authors: V
authors: Verhaert
authors: Bart
authors: B
authors: Haex
authors: Tom
authors: T
authors: De Wilde
authors: Daniel
authors: D
authors: Berckmans
authors: Marie
authors: M
authors: Vandekerckhove
authors: Johan
authors: J
authors: Verbraecken
authors: Jos
authors: J
authors: Vander Sloten
mesh_terms: D000465:Algorithms; D001513:Beds; D006801:Humans; D009068:Movement; D011187:Posture; D012890:Sleep