Mid Sweden University

miun.sePublications
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
H-Watch: An open, connected platform for AI-enhanced CoViD19 infection symptoms monitoring and contact tracing.
Show others and affiliations
2021 (English)In: Proceedings - IEEE International Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers Inc. , 2021Conference paper (Refereed)
Abstract [en]

The novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch. © 2021 IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021.
Keywords [en]
COVID-19, Low power design, Smart sensors, Tiny machine learning, Wearable device, Wireless sensors networks, Data handling, Electric batteries, Open source software, Open systems, Radio transmission, Turing machines, Watches, Wearable computers, Battery lifetime, Contact tracing, Design and implementations, Energy Harvester, Research communities, Symptom detections, Wearable devices, Wireless transmissions, Wearable sensors
Identifiers
URN: urn:nbn:se:miun:diva-43095DOI: 10.1109/ISCAS51556.2021.9401362Scopus ID: 2-s2.0-85108990317ISBN: 9781728192017 (print)OAI: oai:DiVA.org:miun-43095DiVA, id: diva2:1595711
Note

Cited By :1; Export Date: 20 September 2021; Conference Paper; CODEN: PICSD

Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2021-09-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Magno, M.

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 3 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