Evidence map›Paper›PMID 33167556›Full record

ArticleSensors (Basel, Switzerland)2020

Non-Contact Respiration Monitoring and Body Movements Detection for Sleep Using Thermal Imaging.

Prasara Jakkaew, Takao Onoye

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
3.0field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Flow-Field Inference for Turbulent Exhale Flow Measurement.Diagnostics (Basel, Switzerland) · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Contactless Camera-Based Sleep Staging: The HealthBed Study.Bioengineering (Basel, Switzerland) · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

Prasara JakkaewInformation Systems Synthesis Laboratory, Department of Information Systems Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.ORCID 0000-0003-4708-131X
Takao OnoyeInformation Systems Synthesis Laboratory, Department of Information Systems Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.ORCID 0000-0002-1894-2448
Mae Fah Luang University · THThe University of Osaka · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of 1.82±0.75 bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves.

Indexed as

Respiratory RateSignal Processing, Computer-AssistedSleepAdultFemaleHumansMaleMonitoring, PhysiologicRespirationbody movements detectionnatural sleep environmentsnon-contact monitoringrespiration monitoringthermal imaging

Identifiers

PMID33167556
PMCPMC7663997
OpenAlexW3095291798

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.