hh.sePublications
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
Identification of Gait Events using Expert Knowledge and Continuous Wavelet Transform Analysis
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-4086-9221
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4143-2948
2014 (English)In: BIOSIGNALS 2014: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing / [ed] Harald Loose, Guy Plantier, Tanja Schultz, Ana Fred & Hugo Gamboa, [S.l.]: SciTePress, 2014, 197-204 p.Conference paper, (Refereed)
Abstract [en]

Many gait analysis applications involve long-term or continuous monitoring which require gait measurements to be taken outdoors. Wearable inertial sensors like accelerometers have become popular for such applications as they are miniature, low-powered and inexpensive but with the drawback that they are prone to noise and require robust algorithms for precise identification of gait events. However, most gait event detection algorithms have been developed by simulating physical world environments inside controlled laboratories. In this paper, we propose a novel algorithm that robustly and efficiently identifies gait events from accelerometer signals collected during both, indoor and outdoor walking of healthy subjects. The proposed method makes adept use of prior knowledge of walking gait characteristics, referred to as expert knowledge, in conjunction with continuous wavelet transform analysis to detect gait events of heel strike and toe off. It was observed that in comparison to indoor, the outdoor walking acceleration signals were of poorer quality and highly corrupted with noise. The proposed algorithm presents an automated way to effectively analyze such noisy signals in order to identify gait events.

Place, publisher, year, edition, pages
[S.l.]: SciTePress, 2014. 197-204 p.
Keyword [en]
Gait Event Detection, Gait Event Identification, Wavelet Analysis, Accelerometers, Outdoor Walking, Continuous Wavelet Transform, Inertial Sensors, Expert Knowledge
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-24396DOI: 10.5220/0004799801970204Scopus ID: 2-s2.0-84902342535ISBN: 978-989-758-011-6 OAI: oai:DiVA.org:hh-24396DiVA: diva2:688909
Conference
7th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2014), Angers, France, March 3-6, 2014
Available from: 2014-01-18 Created: 2014-01-18 Last updated: 2016-03-09Bibliographically approved

Open Access in DiVA

Khandelwal_biosignals_2014(432 kB)496 downloads
File information
File name FULLTEXT01.pdfFile size 432 kBChecksum SHA-512
15dfba3fd3841df3785ebd3e18fe78e07ccf877ac03c233d662afd5d7cc646d2b7061de3c20cee5bc62f42a6bc4f8cd2f22586f9955f5d4225163f1626be827a
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Khandelwal, SiddharthaWickström, Nicholas
By organisation
CAISR - Center for Applied Intelligent Systems Research
Other Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 496 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 701 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