Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
IoT-Based Remote Pain Monitoring System: From Device to Cloud Platform
Zhejiang Univ, Coll Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310058, Zhejiang, Peoples R China..
Univ Turku, Dept Future Technol, SF-20500 Turku, Finland..
Inst Pasteur, Imaging & Modeling Unit, F-75015 Paris, France..
KTH, School of Technology and Health (STH).
Show others and affiliations
2018 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 6, p. 1711-1719Article in journal (Refereed) Published
Abstract [en]

Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the intensive care unit and minors. In this paper, a wearable device with a biosensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things (IoT) system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system acts as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 22, no 6, p. 1711-1719
Keywords [en]
Biopotential sensor node, cloud computing, healthcare internet-of-things (IoT), pain assessment, wearable sensors, web-based UI for IoT applications
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-238524DOI: 10.1109/JBHI.2017.2776351ISI: 000447833100002PubMedID: 29990259Scopus ID: 2-s2.0-85035791208OAI: oai:DiVA.org:kth-238524DiVA, id: diva2:1261015
Note

QC 20181106

Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2020-03-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Search in DiVA

By author/editor
Ji, GuangchaoTenhunen, Hannu
By organisation
School of Technology and Health (STH)
In the same journal
IEEE journal of biomedical and health informatics
Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 52 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf