Literature DB >> 19171523

Detection of movement in bed using unobtrusive load cell sensors.

Adriana M Adami1, Misha Pavel, Tamara L Hayes, Clifford M Singer.   

Abstract

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.

Mesh:

Year:  2009        PMID: 19171523     DOI: 10.1109/TITB.2008.2010701

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  10 in total

1.  Estimation of rest-activity patterns using motion sensors.

Authors:  Tamara L Hayes; Thomas Riley; Misha Pavel; Jeffrey A Kaye
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

2.  A subject state detection approach to determine rest-activity patterns using load cells.

Authors:  Adriana M Adami; Andre G Adami; Gilmar Schwarz; Zachary T Beattie; Tamara L Hayes
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

3.  Classification of lying position using load cells under the bed.

Authors:  Zachary T Beattie; Chad C Hagen; Tamara L Hayes
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

4.  Classification of breathing events using load cells under the bed.

Authors:  Zachary T Beattie; Chad C Hagen; Misha Pavel; Tamara L Hayes
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

5.  Unobtrusive classification of sleep and wakefulness using load cells under the bed.

Authors:  Daniel Austin; Zachary T Beattie; Thomas Riley; Adriana M Adami; Chad C Hagen; Tamara L Hayes
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

6.  A Gaussian model for movement detection during sleep.

Authors:  Adriana M Adami; André G Adami; Tamara L Hayes; Misha Pavel; Zachary T Beattie
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

7.  Hands-On Experiences in Deploying Cost-Effective Ambient-Assisted Living Systems.

Authors:  Athanasios Dasios; Damianos Gavalas; Grammati Pantziou; Charalampos Konstantopoulos
Journal:  Sensors (Basel)       Date:  2015-06-18       Impact factor: 3.576

8.  homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring.

Authors:  Rosa Ma Alsina-Pagès; Joan Navarro; Francesc Alías; Marcos Hervás
Journal:  Sensors (Basel)       Date:  2017-04-13       Impact factor: 3.576

9.  Logarithmic Strain Model for Nonlinear Load Cell.

Authors:  Young-Dae Hong; Bumjoo Lee
Journal:  Sensors (Basel)       Date:  2019-08-09       Impact factor: 3.576

10.  Real-Time Distributed Architecture for Remote Acoustic Elderly Monitoring in Residential-Scale Ambient Assisted Living Scenarios.

Authors:  Joan Navarro; Ester Vidaña-Vila; Rosa Ma Alsina-Pagès; Marcos Hervás
Journal:  Sensors (Basel)       Date:  2018-08-01       Impact factor: 3.576

  10 in total

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