By Topic

Detection of Movement in Bed Using Unobtrusive Load Cell Sensors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Adriana M. Adami ; Oregon Health and Science University, Portland, USA ; Misha Pavel ; Tamara L. Hayes ; Clifford M. Singer

Quality of sleep is an important attribute of an individual's health state and its assessment is therefore a useful diagnostic feature. Changes in the patterns of motor activities during sleep can be a disease marker, or can reflect various abnormal physiological and neurological conditions. Presently, there are no convenient, unobtrusive ways to assess quality of sleep outside of a clinic. This paper describes a system for unobtrusive detection of movement in bed that uses load cells installed at the corners of a bed. The system focuses on identifying when a movement occurs based on the forces sensed by the load cells. The movement detection approach estimates the energy in each load cell signal over short segments to capture the variations caused by movement. The accuracy of the detector is evaluated using data collected in the laboratory. The detector is capable of detecting voluntary movements in bed while the subjects were awake, with an average equal error rate of 3.22% (??0.54). Its performance is invariant with respect to the individual's characteristics, e.g., weight, as well as those of the bed. The simplicity of the resulting algorithms and their relative insensitivity to the weight and height of the monitored individual make the approach practical and easily deployable in residential and clinical settings.

Published in:

IEEE Transactions on Information Technology in Biomedicine  (Volume:14 ,  Issue: 2 )