Objectives: To assess, using complete polysomnography as the gold standard, the capability of Heasys(®), an innovative wireless bed monitoring assistance to record body movements and presence and to infer bed-exit events and body position changes at night.
Design: Descriptive study.
Settings: Sleep laboratory for patient's recording and home for healthy volunteers.
Participants: Twelve patients referred for suspicion or treatment of sleep disordered breathing and 5 healthy subjects.
Measurements: Complete polysomnography was recorded during one night in patients and during two nights in healthy volunteers. Heasys(®) sheet was placed under the fitted bed sheet to allow concomitant recording. During the second night, healthy subjects were asked to get out of bed at least 2 times for a minimal duration of 3 min.
Results: Heasys(®) allowed the detection of all bed-exit events in patients and volunteers (sensitivity: 100%, and specificity: 85%). When bed-exit events were defined by the lack of the presence signal combined with absence of motion and a dip in temperature, sensitivity and specificity of Heasys(®) were 92 and 100%, respectively. In patients and volunteers, Heasys(®) detected body position changes recorded by polysomnography respectively, in 84 and 98% of the cases. Additional recorded motions were mainly related to leg movements or arousals.
Conclusion: In this small feasibility study, Heasys(®) seemed to be an effective innovative device allowing bed-exit events detection in adult patients and healthy volunteers.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ijmedinf.2010.10.007 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care.
Sensors (Basel)
February 2023
Gerontechnology Research Center, Yuan Ze University, Taoyuan 320, Taiwan.
Article Synopsis
- The study explores a motion-sensing mattress developed to prevent patient falls in care settings by detecting bed-exit intentions through pressure patterns from 30 sensing areas.
- It analyzes how sleep-related data from the mattress can provide comprehensive care information, improve caregiver support, and enhance monitoring of residents' wellbeing.
- Field trials conducted in a dementia nursing home in Taiwan demonstrated that the smart mattress care system (SMCS) effectively alerted caregivers to residents’ sleep patterns and abnormalities, ultimately aiding in better communication and care quality.
Wireless Sensor Network for Fall Prevention on Geriatric Wards: A Report.
Stud Health Technol Inform
August 2019
Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Germany.
With regard to the growing number of older adults, it needs smart solutions for fall prevention. Especially at geriatric institutions, the risk of falling is very high and frequently leads to injuries, resulting in serious consequences. We present the Inexpensive Node for Bed Exit Detection (INBED), a comprehensive signaling system for fall prevention.
View Article and Find Full Text PDFINBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward.
Sensors (Basel)
February 2019
Institute of Computer Engineering, Technical University of Braunschweig, D-38106 Braunschweig, Germany.
Objective: In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving.
Methods: Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction.
Reducing medical-surgical inpatient falls and injuries with videos, icons and alarms.
BMJ Open Qual
October 2017
Department of Nursing, City College of San Francisco, San Francisco, California, USA.
Background: Inpatient falls and subsequent injuries are among the most common hospital-acquired conditions with few effective prevention methods.
Objective: To evaluate the effectiveness of patient education videos and fall prevention visual signalling icons when added to bed exit alarms in improving acutely hospitalised medical-surgical inpatient fall and injury rates.
Design: Performance improvement study with historic control.
DeepPredict: A deep predictive intelligence platform for patient monitoring.
Annu Int Conf IEEE Eng Med Biol Soc
July 2017
A novel platform, DeepPredict, for predicting hospital bed exit events from video camera systems is proposed. DeepPredict processes video data with a deep convolutional neural network consisting of five main layers: a 1 × 1 3D convolutional layer used for generating feature maps from raw video data, a context-aware pooling layer used for rectifying data from different camera angles, two fully connected layers used for applying pre-trained deep features, and an output layer used to provide a likelihood of a bed exit event. Results for a model trained on 180 hours of data demonstrate accuracy, sensitivity, and specificity of 86.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!