Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Robust Sleep Quality Quantification Method for a Personal Handheld Device

Authors
조재걸
Issue Date
Jun-2014
Publisher
Mary Ann Liebert Inc.
Keywords
sleep monitoring; sleep efficiency; sleep quality; wavelet; healthcare application
Citation
Telemedicine and e-Health, v.20, no.6, pp 522 - 530
Pages
9
Journal Title
Telemedicine and e-Health
Volume
20
Number
6
Start Page
522
End Page
530
URI
https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/12138
ISSN
1530-5627
1556-3669
Abstract
Objective: The purpose of this study was to develop and validate a novel method for sleep quality quantification using personal handheld devices. Materials and Methods: The proposed method used 3- or 6-axes signals, including acceleration and angular velocity, obtained from built-in sensors in a smartphone and applied a real-time wavelet denoising technique to minimize the nonstationary noise. Sleep or wake status was decided on each axis, and the totals were finally summed to calculate sleep efficiency (SE), regarded as sleep quality in general. The sleep experiment was carried out for performance evaluation of the proposed method, and 14 subjects participated. An experimental protocol was designed for comparative analysis. The activity during sleep was recorded not only by the proposed method but also by well-known commercial applications simultaneously; moreover, activity was recorded on different mattresses and locations to verify the reliability in practical use. Every calculated SE was compared with the SE of a clinically certified medical device, the Philips (Amsterdam, The Netherlands) Actiwatch. Results: In these experiments, the proposed method proved its reliability in quantifying sleep quality. Compared with the Actiwatch, accuracy and average bias error of SE calculated by the proposed method were 96.50% and -1.91%, respectively. Conclusions: The proposed method was vastly superior to other comparative applications with at least 11.41% in average accuracy and at least 6.10% in average bias; average accuracy and average absolute bias error of comparative applications were 76.33% and 17.52%, respectively.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Medical Sciences > Department of Biomedical Mechatronics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Cho, JAE GEOL photo

Cho, JAE GEOL
College of Medical Sciences (의공학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE