Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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 Fall Detection System with Energy Efficient Sensor Nodes
Show others and affiliations
2016 (English)In: 2016 2ND IEEE NORDIC CIRCUITS AND SYSTEMS CONFERENCE (NORCAS), IEEE conference proceedings, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Fall needs to be attentively considered due to its highly frequent occurrence especially with old people - up to one third of 65 and above year-old people around the world are risk of being injured due to falling. Furthermore, fall is a direct or indirect factor causing severe traumas such as brain injuries or bone fractures. However, timely medical attention might help to avoid serious consequences from a fall. A viable solution to solve this is an IoT-based system which takes advantage of wireless sensor networks, wearable devices, Fog and Cloud computing. To deliver sufficient degree of reliability, wearable devices working at the core of a fall detection system, are required to work for prolonged period of time. In this paper we investigate energy consumption of sensor nodes in an IoT-based fall detection system and present a design of a customized sensor node. In addition, we compare the customized sensor node with other sensor nodes, built on general purpose development boards. The results show that sensor nodes based on delicate customized devices are more energy efficient than the others based on general purpose devices while considering identical specification of micro-controller and memory capacity. Furthermore, our customized sensor node with energy efficiency selections can operate continuously up to 35 hours.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016.
Keywords [en]
Internet-of-Things, Fall Detection, Fog Computing, Energy Efficiency, Wearable Devices
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-201280DOI: 10.1109/NORCHIP.2016.7792890ISI: 000391620400017Scopus ID: 2-s2.0-85011044718ISBN: 978-1-5090-1095-0 (print)OAI: oai:DiVA.org:kth-201280DiVA, id: diva2:1074238
Conference
2nd IEEE Nordic Circuits and Systems Conference (NORCAS), NOV 01-02, 2016, Copenhagen, DENMARK
Note

QC 20170215

Available from: 2017-02-15 Created: 2017-02-15 Last updated: 2017-02-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Tenhunen, Hannu

Search in DiVA

By author/editor
Tenhunen, Hannu
By organisation
Industrial and Medical Electronics
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 21 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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